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    "# J(oint)-PCMCI$^+$: Causal discovery for time series from multiple (possibly latent) contexts\n",
    "\n",
    "TIGRAMITE is a time series analysis python module. It allows to reconstruct causal graphical models from discrete or continuously-valued time series based on the PCMCI framework and create high-quality plots of the results.\n",
    "\n",
    "This tutorial explains the Joint-PCMCI$^+$ (J-PCMCI$^+$) algorithm, which is implemented as the function ```J_PCMCI.run_jpcmciplus```. J-PCMCI$^+$ is specifically designed to handle time series data from different datasets that include time series observations of the same variables (system-variables), but that were collected in different contexts. While also the PCMCI and PCMCIplus algorithms can handle multiple datasets by simply pooling them, J-PCMCI+ allows to characterize the contexts by specific context-variables and can learn 1) how these contexts cause the system variables and 2) deconfound spurious system-variable links due to observed as well as latent contexts.\n",
    "\n",
    "* * * * *\n",
    "Publication on J-PCMCI$^+$: Günther, Wiebke, and Ninad, Urmi and Runge, Jakob (2023). Causal discovery for time series from multiple datasets with latent contexts. Accepted at 39th Conference on Uncertainty in Artificial Intelligence 2023.\n",
    "https://arxiv.org/abs/2306.12896\n",
    "* * * * *\n",
    "\n",
    "The tutorial covers the following:\n",
    "1. Motivation and basic concepts\n",
    "2. The algorithm\n",
    "3. Application example"
   ]
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    "# Imports\n",
    "import numpy as np\n",
    "from numpy.random import SeedSequence\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import tigramite\n",
    "from tigramite.toymodels import structural_causal_processes as toys\n",
    "from tigramite.toymodels.context_model import ContextModel\n",
    "from tigramite.jpcmciplus import JPCMCIplus\n",
    "from tigramite.independence_tests.parcorr_mult import ParCorrMult\n",
    "from tigramite.independence_tests.regressionCI import RegressionCI\n",
    "import tigramite.data_processing as pp\n",
    "import tigramite.plotting as tp"
   ]
  },
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    "## 1. Motivation and basic concepts\n",
    "\n",
    "We are interested in discovering the causal graph between variables that we have observed within different contexts, i.e., we have different datasets with observational time series of these variables (see schematic below). For instance, such contexts can be different locations at which we have observed these variables, such as time series of river runoff and local weather variables from different catchments. \n",
    "\n",
    "<img style=\"float: left; margin:10px 30px 0 0\" src=\"./figures/intro_jpcmci.png\" alt= “” width=\"30%\" height=\"30%\">\n",
    "\n",
    "In this example, the local catchment systems share certain causal influences, such as time-dependent large-scale weather over all catchments, but differ in other catchment-specific but constant-in-time drivers, such as the altitude of the catchment. We call these drivers ***temporal*** and ***spatial contexts*** ($\\tilde{C}_\\text{time}$ and $\\tilde{C}_\\text{space}$ in the schematic), respectively. They differ from the ***system variables*** (runoff, local weather variables such as precipitation) in the sense that some background knowledge on their structure and on their relationship to the system variables is available. In particuar, we assume the context variables to be either constant in time and varying over the datasets, or varying in time and constant over the datasets. Furthermore, we assume them to be exogenous to the system variables.\n",
    "\n",
    "These assumptions allow us to learn how contexts affect system variables as well as overcome latent confounding that is due to unobserved context variables by pooling the datasets and considering the joint causal graph among system, context, and certain auxiliary variables as we will see in more detail in the following sections of this tutorial.\n",
    "\n",
    "\n",
    "\n",
    "***To summarize***, in this tutorial we take a look at J-PCMCI$^+$, which is a consistent causal discovery algorithm that can:\n",
    "\n",
    "   1. de-confound those system nodes that are confounded by latent contexts without having any knowledge of the latent contexts themselves;\n",
    "   2. retain as much information about the causal links between the observed  context and system variables as possible by checking conditional independencies appropriately;\n",
    "   3. discover the correct induced causal graph between the system nodes."
   ]
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    "### 1.1 The joint time-dependent  structural causal model\n",
    "\n",
    "To formalize the ideas presented above, we assume that the observed data was generated by an underlying mechanism across datasets $d\\in \\mathcal{D}$ with $|\\mathcal{D}|=M$ that can be represented by an acyclic time-dependent structural causal model (SCM) involving the time-dependent system variables $\\mathbf{X}_t=\\{X_t^i\\}_{i\\in \\mathcal{I}}$ at time $t$ as well as context variables $\\tilde{\\mathbf{C}}=\\tilde{\\mathbf{C}}_{time}\\dot{\\cup} \\ \n",
    "    \\tilde{\\mathbf{C}}_{space}$ with temporal contexts $\\tilde{\\mathbf{C}}_{time,t}=\\{\\tilde{C}_t^k\\}_{k\\in \\mathcal{K}_{\\rm time}}$ and (time-independent) spatial-contexts $\\tilde{\\mathbf{C}}_{space}=\\{\\tilde{C}^l\\}_{l\\in \\mathcal{K}_{\\rm space}}$, for $i\\in \\mathcal{I}$:\n",
    "    \n",
    "\\begin{aligned}\n",
    "            &\\mathbf{X}^{d}_t:= \\mathbf{f}({Pa}_X(\\mathbf{X}^{d}_t),\\operatorname{Pa}_{\\tilde{C}_{\\rm time}}(\\mathbf{X}^{d}_t), \\operatorname{Pa}_{\\tilde{C}_{\\rm space}}(\\mathbf{X}^{d}_t), \\boldsymbol{\\eta}^{d}_t) \\\\ \n",
    "            &\\mathbf{\\tilde{C}}_{{\\rm time}, t} := \\mathbf{g}({Pa}_{\\tilde{C}_{\\rm time}}(\\mathbf{\\tilde{C}}_{{\\rm time}, t}), \\boldsymbol{\\eta}_{{\\rm time}, t}) \\\\\n",
    "            &\\mathbf{\\tilde{C}}^{d}_{\\rm space} := \\mathbf{h}({Pa}_{\\tilde{C}_{\\rm space}}(\\mathbf{\\tilde{C}}_{\\rm space}), \\boldsymbol{\\eta}_{\\rm space}^{d})\n",
    "\\end{aligned}\n",
    " where the exogenous noise variables $(\\boldsymbol{\\eta}^{d}_t, \\boldsymbol{\\eta}_{{\\rm time}, t}, \\boldsymbol{\\eta}_{\\rm space}^{d})$ are jointly independent and $\\eta^{i,d}_{t}$ are identically distributed across time and space, $\\eta^k_{{\\rm time},t}$ are identically distributed across time, and $\\eta^{l,d}_{\\rm space}$ are identically distributed across space. $\\operatorname{Pa}_X$ denotes the causal parents within $\\mathbf{X}$, and analogously for $\\operatorname{Pa}_{\\tilde{C}_{\\rm time}}$ and $\\operatorname{Pa}_{\\tilde{C}_{\\rm space}}$.\n",
    " \n",
    "Note that we assume that the context variables are exogeneous to the system. In other words, system variables cannot be parents of context variables.\n"
   ]
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    "### 1.2 Context variables and dummy variables\n",
    "\n",
    "Let us take a closer look at the context variables $\\tilde{\\mathbf{C}}_\\text{time}$ and $\\tilde{\\mathbf{C}}_\\text{space}$ in the SCM above. \n",
    "\n",
    "A temporal context variable $C^i_\\text{time} \\in \\tilde{\\mathbf{C}}_\\text{time}$ is a time-dependent random variable that remains the same across datasets. A spatial context variable $C^i_\\text{space} \\in \\tilde{\\mathbf{C}}_\\text{space}$ is a random variable that is constant over time and within a dataset but can change across datasets. (A context that various over both time and datasets is rather a system variable.) In our motivational example,$C^i_\\text{time}$ could represent large-scale weather dynamics that affect multiple observational sites at once. Another example could be the economic development in a country over a time that affects all individuals living in that country. An example for $C^i_\\text{space}$ could be the average slope in a river catchment or the height of an individual. \n",
    "We think of the context variables as corresponding to real-world phenomena. \n",
    "\n",
    "However, J-PCMCI+ can also deal with unobserved context variables. This is achieved through auxiliary variables, so-called dummies that mimic the structure of context variables. These dummy variables label each time-step or dataset, respectively (and thus the time dummy has a unique value for each time step, and the space dummy has a unique value within each dataset). Generally, their values can not be interpreted as a physical quantity. In that sense, the dummy variable is not a causal variable itself. They are intended to be a placeholder for the unobserved context variables, in order to capture confounding between system variables due to latent context variables. \n",
    "\n",
    "Within J-PCMCI+, the dummy variables are encoded using one-hot vectors, i.e., for the time dummy the first time point has the corresponding dummy-value $(1,0,\\ldots,0)^T \\in \\{ 0,1 \\}^T$, the second time point corresponds to $(0,1,\\ldots,0)^T$, and so on. For the space dummy dataset $1$ corresponds to the dummy-value $(1,0,\\ldots,0)^T \\in \\{ 0,1 \\}^M$, etc."
   ]
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    "## 2. The algorithm\n",
    "\n",
    "J-PCMCI+ extends the PCMCI+ time series causal discovery algorithm to the case of datasets from multiple dataset- or time dependent contexts with potentially unobserved context confounders of the system variables. It does so by combining PCMCI+ with the JCI-approach [1], i.e., pooling datasets from multiple contexts, and adding observed context variables to the graph. Furthermore, it uses time- and space-dummy variables to account for latent context variables that confound system variables, similar to [2].\n",
    "\n",
    "\n",
    "The method consists of four main steps: one $\\text{PC}_1$ lagged phase and three MCI phases similar to PCMCI+. \n",
    "\n",
    "1. ***$\\text{PC}_1$ lagged discovery phase: Discovery of supersets of the lagged parents of the system and observed temporal context nodes on all pairs $((C^i_{t-\\tau}, X^j_t))_{\\tau > 0}$ and $((X^i_{t-\\tau}, X^j_t))_{\\tau > 0}$, as well as $((C^i_{t-\\tau}, C^i_t))_{\\tau > 0}$.*** (The last set of pairs covers autocorrelation in time-contexts, spatial context nodes do not carry temporal information and can be ignored here.)\n",
    "\n",
    "2. ***MCI skeleton phase on context-system pairs conditional on subsets of system and context nodes***, i.e. perform MCI tests for pairs $((C^i_{t-\\tau}, X^j_t))_{\\tau \\geq 0}$,\n",
    "   $$\n",
    "         C_{t-\\tau}^i \\perp\\!\\!\\!\\!\\perp X_t^j | \\mathbf{S}, \\hat{\\mathcal{B}}^-_t(X_t^j)  \\setminus \\{ C_{t-\\tau}^i \\}, \\hat{\\mathcal{B}}^-_{t-\\tau}(C_{t-\\tau}^i)\n",
    "   $$\n",
    "with $\\mathbf{S}$ denoting a subset of the contemporaneous adjacencies $\\mathcal{A}_t(X_t^j)$ (among system or time-context variables) and where $\\hat{\\mathcal{B}}^-_t(X_t^j)$ are the lagged adjacencies from step one. If $C$ is a spatial context variable, we only have to test the contemporaneous pairs $(C_t^j, X_t^i)$, $(X_t^i, C_t^j)$ for all $i,j$. Remember that the space-context nodes are constant over time and therefore essentially don't have a time-dimension associated with them (so we could also drop the time index, i.e. $C^j_t = C^j$). That means there is an edge $C^j \\rightarrow X_{t}^i$ in the joint time series graph if and only if there also exist edges $C^j \\rightarrow X_{t-\\tau}^i$ for all $\\tau > 0$. Thus, if $C^j$ and $X_t^i$ are conditionally independent, all these links between $C^j$ and $X^i_{t-\\tau}$ are also removed for all $\\tau$.\n",
    "\n",
    "3. ***MCI skeleton phase on all system-dummy pairs conditional on the superset of lagged links, the discovered contemporaneous context adjacencies, as well as on subsets of contemporaneous system links***, i.e. test for $(D, X_t^i)$, $(X_t^i, D)$ for all $i$ where $D$ stands for the time- and space-dummy respectively, i.e. test\n",
    "    $$\n",
    "    D \\perp\\!\\!\\!\\!\\perp X_t^j | \\mathbf{S}, \\hat{\\mathcal{B}}^C_t(X_t^j)\n",
    "    $$\n",
    "    where $\\mathbf{S} \\subset \\mathcal{A}_t(X_t^i)$ and $\\hat{\\mathcal{B}}^C_t(X_t^j)$ are the lagged and contextual adjacencies found in the previous step.\n",
    "If $D$ and $X_t^j$ are found to be conditionally independence, links between $D$ and $X^j_{t-\\tau}$ are removed for all $\\tau$.\n",
    "Using the exogeneity assumption, the context node is the parent in all system-context links.\n",
    "\n",
    "4. ***MCI skeleton phase on all system pairs conditional on discovered lagged, context and dummy adjacencies, as well as on subsets of contemporaneous system links and orientation phase***. In more detail, we perform MCI test for pairs $((X^j_{t-\\tau}, X_t^i))_{\\tau \\geq 0}$, $(X_t^i, X_t^j)$ for all $i, j$, i.e.\n",
    "    $$\n",
    "     X^i_{t-\\tau} \\perp\\!\\!\\!\\!\\perp X_t^j | \\mathbf{S}, \\hat{\\mathcal{B}}^{CD}_t(X_t^j)  \\setminus \\{ X_{t-\\tau}^i \\},  \\hat{\\mathcal{B}}^{CD}_t(X_{t-\\tau}^i) \n",
    "    $$\n",
    "    where $\\mathbf{S} \\subset \\mathcal{A}_t(X_t^i)$ and $\\hat{\\mathcal{B}}^{CD}_t(X_t^j)$ are the lagged, contextual, and dummy adjacencies found in the previous steps.\n",
    "Finally, all remaining edges (without expert knowledge) are oriented using the PCMCI$^+$ orientation phase while making use of all triples involving at most one context or dummy variable and two or three system variables as in the non-time series case."
   ]
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    "## 3. Application Example\n",
    "This section demonstrates and explains the application of J-PCMCI+ on synthetic data.\n",
    "\n",
    "### 3.1 Setup \n",
    "Here we choose the random seed, how large of a sample we want to generate, and some other hyperparameters."
   ]
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    "# Set seeds for reproducibility\n",
    "seed = 12345\n",
    "ss = SeedSequence(seed)\n",
    "noise_seed = ss.spawn(1)[0]\n",
    "\n",
    "random_state = np.random.default_rng(noise_seed)\n",
    "\n",
    "# Choose the time series length and number of spatial contexts\n",
    "T = 100\n",
    "nb_domains = 50\n",
    "\n",
    "transient_fraction=0.2\n",
    "tau_max = 2\n",
    "frac_observed = 0.5"
   ]
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    "### 3.2 Data generation\n",
    "For generating synthetic data, we consider a specific version of the time-dependent SCM (1). Namely, within each dataset $d$ at time point $t$:\n",
    "\\begin{aligned}\n",
    "X^{1,d}_t &= 0.3X^{1,d}_{t-1} + 0.6C^4_t + 0.9L_{t-1}^5 + \\eta^{1,d}_t, \\\\\n",
    "X^{2,d}_t &= 0.4X^{2,d}_{t-1} + 0.4C^4_{t-1} + \\eta^{2,d}_t, \\\\\n",
    "X^{3,d}_t &= 0.3X^{3,d}_{t-1} - 0.5X^{2,d}_{t-2} + 0.5L_{t-1}^5 + 0.6C^6 + \\eta^{3,d}_t, \\\\\n",
    "C^4_t &= \\eta^4_t, \\\\\n",
    "L_t^{5} &= \\eta^{5}_t, \\\\\n",
    "C^{6,d} &= \\eta^{5,d}, \\\\\n",
    "\\end{aligned}\n",
    "where the temporal context variables $C_t^4$ and $L_t^5$ have the same value within each dataset, and the spatial context variable $C^6$ is constant over time, $L_t^5$ is a hidden temporal context."
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    "# Specify the model\n",
    "def lin(x): return x\n",
    "\n",
    "links = {0: [((0, -1), 0.3, lin), ((3, -1), 0.6, lin), ((4, -1), 0.9, lin)],\n",
    "         1: [((1, -1), 0.4, lin), ((3, -1), 0.4, lin)],\n",
    "         2: [((2, -1), 0.3, lin), ((1, -2), -0.5, lin), ((4, -1), 0.5, lin), ((5, 0), 0.6, lin)] ,\n",
    "         3: [], \n",
    "         4: [], \n",
    "         5: []\n",
    "            }\n",
    "\n",
    "# Specify which node is a context node via node_type (can be \"system\", \"time_context\", or \"space_context\")\n",
    "node_classification = {\n",
    "    0: \"system\",\n",
    "    1: \"system\",\n",
    "    2: \"system\",\n",
    "    3: \"time_context\",\n",
    "    4: \"time_context\",\n",
    "    5: \"space_context\"\n",
    "}\n",
    "\n",
    "# Specify dynamical noise term distributions, here unit variance Gaussians\n",
    "noises = [random_state.standard_normal for j in range(6)]\n",
    "\n",
    "contextmodel = ContextModel(links=links, node_classification=node_classification,\n",
    "                            noises=noises, \n",
    "                            seed=seed)\n",
    "\n",
    "data_ens, nonstationary = contextmodel.generate_data(nb_domains, T)\n",
    "\n",
    "assert not nonstationary\n",
    "\n",
    "system_indices = [0,1,2]\n",
    "# decide which context variables should be latent, and which are observed\n",
    "observed_indices_time = [4]\n",
    "latent_indices_time = [3]\n",
    "\n",
    "observed_indices_space = [5]\n",
    "latent_indices_space = []\n",
    "\n",
    "# all system variables are also observed, thus we get the following observed data\n",
    "observed_indices = system_indices + observed_indices_time + observed_indices_space\n",
    "data_observed = {key: data_ens[key][:,observed_indices] for key in data_ens}"
   ]
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    "We have a quick look at the ground truth graph that we now have specified and which corresponds to the SCM (1) above. For that we use the function ```tp.plot_graph```. The observed temporal and spatial context nodes are labelled as t-$C_i$ and s-$C_i$, respectively. Their latent counterparts are labelled as t-$L_i$ and s-$L_i$, respectively."
   ]
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dIZFI1LpSo729PQUBIjcaGSCspaWlqSUQmJubw8XFhbM37k2bNmHatGmVPnrYvn17rFq1Sra74Z07dxAfH8/JeYlqubm5lbv4u7q6quTxOoZhkJSUpJZAYGdnp3PLZhPlUBggCnn79i2SkpJU9gx/fn4+goKCsGTJEk5XUDt58iRGjBhRYcGd+fPn48cffyz3vczMTISHh5cLCNXxCQVtpa+vj88++wyNGjWCt7e37J/qnEXPMAwyMjJUNodAJBLB2dlZa1a+JJqDwgBRWElJCZKSknh9lEpPTw+1atWCSCSCsbEx9PT08M033+D7779HjRo1ODnH/fv30adPH/z333+y7509exY9e/b8ZNv09HTExsYiNjYWMTExsj8nJSVxUpuqlS3prOlcXV0rXPQ9PDwgFmvmnc+CggIkJiaipKSEt3OYmJjA2dlZq7ZTJpqDwgBRStknn7S0NE5XXxMIBLCysoKdnZ1sJrenp6dsqN7S0hLfffcdpk2bxskmQWlpaRg0aBCuXr0KgUCAzMzMjy5m9Cm5ubmIi4srFxBiY2ORkJCgcavUWVpaYsyYMbhz5w7CwsLUXU45FhYWsgt+2UW/YcOGMDc3V3dprEkkErx69QpZWVmcBi6RSARbW1tYW1vT6oJEYRQGCCekUilu3boFU1NTpWZiC4VC2NjYoEaNGhU+5fXp0wenTp0q9z1HR0csWrQIY8eOVfpTYXFxMaZNm4awsDA8ePBAqb4q8+jRI2zZsgV79+7VmEWcevfujcOHD2P06NH466+/VHpugUAAOzs7ODs7w9nZGS4uLrI/Ozs7w83NDc7OztXuAldaWorMzEykp6crdZtNX18fNjY2sLS0pDUEiNIoDBBOxMfHo0OHDrhw4QJq166N3Nxc5OXloaio6JNt9fT0YGxsDFNTU1hYWFT5xjZnzhysWbOm0p95enrip59+wsCBA5W6eDAMgwcPHsDb21vhPt5XVFSEY8eOISgoCKGhoZz0yZV+/frhwIEDGD9+PPbu3ctp30KhEA4ODuUu7h9e8B0cHKCvr8/pebWJVCpFTk4OcnNzkZ+fL9ctBAMDA5iYmMDU1BRmZmbVLigR9aEwQJT27NkzdOjQAcnJyTh06BAGDRok+5lUKkVBQQEKCwshlUrBMAwEAgGEQiH09fVhZGQk9yf6bdu2YcKECR89plmzZli5ciW6dOmi1L+TshISEvDHH39gx44dSEtLU0sNpqamcHd3h5ubG2rWrAmJRIKioiLExsYiMjISBQUFKCgowP3798u1W7duHf755x907twZJiYmsi9jY+Nyf//Y921tbTX2/r2mKi0tRUFBAYqLiyu8VgwNDWFkZEQjAIQ39GolSklKSoKfnx+Sk5MBAHFxceV+LhQKZRcIZXl5eX3ymLt376Jr167o1q0bVq5cCV9fX6XPK6+SkhKcPHkSQUFBCAkJUdl53+fh4YH+/fujf//+aNGiRaUXj8OHD2PIkCGYMmVKpX08evQIrVq1wokTJ/gul7xHLBbTUwBEbWhkgCjs5cuX6NixY7nn7wMCAjgfci6TlpaGmjVrsmozdOhQLFu2DHXr1uWlJgD477//sHXrVvz5559qWYa2efPm6NevH/r374969ep9cui4sLAQs2fPxr179yr9eY0aNbB48WI0b96cj3IJIRqIwgBRSFpaGjp16oSYmJhy32/atCnu3r3LyzkZhoG1tTXrZV7FYjEmTJiAhQsXcvbMeWlpKf7++28EBQXh3LlzKn8cz9vbG4GBgRg2bBhcXFxUem5CSPVDYYCwlpmZCT8/P0RHR1f4mYmJCd6+fcvbxKbWrVvj1q1bCrU1NjbGjBkzMGfOHFhYWCjUR0pKCrZu3Ypt27apfJ8GFxcXjBgxAgEBAWjUqJFKz00Iqd4oDBBWcnJy0LVr149++k9KSuJ0lcD3jRkzBjt37lSqjxo1auB///sfpkyZAkNDQ7naREdHY+3atdi/f79Kt3K2sLDAkCFDEBAQgPbt29MEMkIIL+idhcgtNzcX/v7+n7wN8OEkQi7JM4nwUzIzMzFr1ix4enri0qVLVR7HMAzOnTuHbt26wcfHB3v27FFJENDX18eAAQNw5MgRpKam4o8//kDHjh0pCBBCeENPExC5FBYWok+fPrhx48Ynj42NjUXXrl15qYOLMFBm8ODBaNu2bYXvFxUVYd++fVi3bh0ePnzI2fk+pWPHjggMDMTAgQNptzlCiEpRGCCfJJFIMGLECFy+fFmu4/kcGahXr57SfTg6OmLXrl0VAktmZia2bNmCjRs3quypgAYNGmDUqFEYPnw4atWqpZJzEkLIhygMkI9iGAZfffUVjh07JncbPsOAu7s7RCLRJ9f3r1mzJpo2bQovLy88ffoUJ0+eBPBu1b1t27bB2tpaduzTp0+xfv16bN++nddNl8oYGBhg8ODBmDRpEtq2bUuryBFC1I7CAPmohQsXYuvWrazaxMbG8lTNu/vpbm5uePLkSZXHODs7IykpCbm5uZBKpbh48aIsDLi7u8uCwK1bt7BmzRocO3ZMJVsxe3p6YvLkyRg1alS5MEIIIepGYYBUaePGjVi2bBnrdi9fvsTr169ZLxAkLy8vr4+GgbS0NNSvXx+PHz/GoUOHyv1s7dq1MDU1RUhIiFzzH5Slp6eHgQMHYtKkSejYsSONAhBCNBJNTyaV2r9/P6ZNm6Zw+8jISA6rKe9T8waKiooQFxdX5af9JUuW8B4E3N3d8fPPPyM5ORn79+9Hp06dKAgQQjQWjQyQCi5cuIDRo0cr1UdkZCR69OjBUUXlcflEAZfEYjH69u2LyZMnw8/Pjx4FJIRoDQoDpJw7d+5gwIABcm2n+jEREREcVVSRpoWB2rVrY8KECRg7diwcHBzUXQ4hhLBGYYDIxMXFwd/fH3l5eUr3pc7bBKrStm1bzJo1C3369IFIJFJ3OYQQojAaxyQA3t1nHzVqFDIyMjjp78mTJ8jJyeGkrw/Z2trC0tKy3Pc6duwIV1dXXs73PqFQiEGDBuHmzZu4fv06+vfvT0GAEKL1KAwQAO+efb916xYiIiKwcuVK+Pn5QV9fX6k+o6KiuCnuAwKBQHarQCwWIyAgABkZGXj+/Dkv5wPebXL0zTffID4+HocOHUKrVq14OxchhKgabVREqpSbmwtHR0e8fftWofbr16/Ht99+y21R/9/o0aNx8eJFGBkZISEhocLPp0+fDmNjYwQGBgIA9u7di/z8fGzYsIHVeRwcHPDNN99g0qRJqFGjBie1E0KIpqE5A6RKT58+VTgIAPxMImQYBidOnEBYWBhevHhR5XGNGzeGubm5bAGkpk2b4s2bN3Kfp2HDhpg1axaGDx8OAwMDpesmhBBNRmGAVOnixYtKted6EuHt27cxe/ZsXL9+/ZPHjh07VqFzdOvWDbNmzUL37t1pXQBCiM6gOQOkSmzCgI+PD44ePYpJkybJJvLFxMRwstb/06dPMXToULRq1UquIKCM8ePHo0ePHhQECCE6heYMkEoVFxfDyspK7ov5d999h5UrVwJ4N5SfkJCA8+fPo1+/fnByclKohoyMDCxbtgybNm1Set0DeVlaWuL+/ftwcXFRyfkIIUQT0G0CUqlbt26x+lTfuXNn2Z8FAgHq1q2LunXrKnTuwsJC/Pbbb/jpp5+QnZ2tUB+Kys7OxqhRo3Dx4kV6ZJAQojPoNgGpFJtbBGKxGO3atVP6nFKpFPv370f9+vUxZ84clQeBMqGhoVi7dq1azk0IIepAYYBU6tKlS3If27JlS5iYmCh1vitXrqBly5YYMWIE5+sFKLJHwPz583ldUpkQQjQJhQFSQWFhIcLDw+U+3s/PT+FzxcXFoW/fvujUqRPu3r2rcD+VEYvFGD9+PB4/fowuXbqwaltSUoKAgABOJkASQoimozBAKggPD0dxcbHcxysSBl6/fo2vvvoKDRs2xMmTJ1m3/5j3Q8DWrVvh7u6OXbt2wcrKilU/cXFxmD17Nqe1EUKIJqIwQCq4du2a3McaGBiwWppXIpFg06ZN8PT0xJYtWyCRSBQpsVJisRjjxo3Do0ePsHXrVri5ucl+5uTkhK1bt7Luc/PmzTh9+jRnNRJCiCaiMEAqYBMG2rZtC0NDQ7mOvXXrFpo3b46pU6dyvonRyJEj8ejRI2zbtg116tSp9JiBAwdizJgxrPseO3YsXr16pWyJhBCisSgMkHIkEglu3Lgh9/HvP1JYlfT0dEyYMAGtW7fmfFVCPz8/3Lt3D7t3764yBLxvw4YNcHd3Z3WOtLQ0jB07FrQkByGkuqIwQMp58OABqzX8PzZfQCqVYuvWrfDy8sK2bdu4KE/ms88+w99//42LFy/C19dX7nZmZmbYu3cv6zUEzpw5g99//51tmYQQohUoDJBy2NwiMDExQfPmzSv92b1799C6dWtMnDgRmZmZXJUHe3t7bN26FVFRUfD391do2eBWrVph4cKFrNvNnj0bMTExrNsRQoimozBAymGz9n/79u2hp6dX7ntZWVn4+uuv0bx5c9y5c4ezukxMTLB48WLEx8dj/PjxEIuVWzxz3rx5aNOmDas2hYWFCAgIQFFRkVLnJoQQTUNhgMgwDMNqZKBTp07l2u7atQteXl74/fffObu/LhQKMXHiRDx58gSLFi2CqakpJ/2KxWLs2bMHZmZmrNpFRUVh/vz5nNRACCGagsIAkXn69Clevnwp9/FljxTev38fHTp0wJdffom0tDTO6unduzcePHiAoKAg2Nvbc9ZvmTp16uC3335j3W7t2rWsVmgkhBBNR2GAyLAZFRAIBPDw8MDMmTPh6+vL6dbCvr6+uHTpEk6dOoUGDRpw1m9lRo4ciSFDhrBqwzAMRo0axelcCEIIUSfawpjIjB8/Hn/++adcx7q6uoJhGPz333+cnd/BwQGrVq3CiBEjFNpPQFFZWVnw9vZGcnIyq3aDBg3CwYMHFZrESAghmoTCAJHx8vLC48ePVX5ekUiEadOmYfHixTA3N1f5+QHg8uXL6NKlC+u5Djt27MCXX37JT1GEEKIiFAYIACAzMxPW1tYqP2+7du2wadMmeHt7q/zcH/ruu++watUqVm1MTU0RFRXFeiEjQgjRJDRngACAyrfrrVmzJnbt2oWrV69qRBAAgB9//BFNmjRh1SY3NxcjR45EaWkpT1URQgj/KAwQAO8WCVIFoVCIr7/+Go8ePcKoUaM06n67vr4+goOD5d5roczNmzfx008/8VQVIYTwj8IAAaCaMNCiRQuEh4fjt99+g6WlJe/nU0T9+vWxbt061u2WLl2Kmzdv8lARIYTwj+YMEABA3bp1kZCQwEvfNWrUwMqVKzFu3DiVPiWgKIZh0KdPH9ZbF9epUwdRUVGsFzIihBB1ozBAkJ2dDSsrK176Hj9+PFasWAEbGxte+ufL69ev0ahRI7x+/ZpVuy+//BI7duzgqSpCCOGH5n9MI7zjY/JgkyZNcPPmTWzdulXrggDwboKjIhf1nTt34tChQzxURAgh/KEwoOOysrIwY8YMzvozMjLC2rVrER4eLluuWFv5+/vj66+/Zt1u0qRJrBcwIoQQdaLbBDrs6tWrCAwMRFJSEif9tW/fHn/++Sc8PDw46U8TFBQUoGnTpoiNjWXVzs/PDyEhIVoxR4IQQuidSgeVlJRgwYIF6Ny5MydBwNjYGL/++itCQ0OrVRAA3o107Nu3r8JWzZ9y6dIlhZ5KIIQQdaCRAR3z9OlTBAQE4NatW5z017lzZ2zbtg116tThpD9NtXr1asydO5dVGz09Pdy+fZv1QkaEEKJqFAZ0SHBwML766iu8fftW6b5MTU2xevVqTJw4USeGwqVSKbp168Z66+L69evj7t27MDY25qkyQghRXvV/Fyd48+YNRo4cicDAQE6CQLdu3fDw4UNMnjxZJ4IA8G7lxF27drF+BDM2Npb1iAIhhKgajQxUc7du3cKIESPw7NkzpfsSi8XYvHkzxo0bp1HLCKvSoUOHMGTIENbt/v77b/j7+/NQESGEKE83PtbpIIlEgmXLlqFdu3acBAEA+OabbzB+/HidDQIAMHjwYIwePZp1uzFjxrBewIgQQlSFwkA1lJSUBD8/PyxYsAASiYSzfrt27cpZX9rs119/ZT1h8vXr1xg7dixoII4QookoDFQzhw8fhre3N65evcp5340aNeK8T21kbm6OvXv3QiQSsWr3999/Y8uWLTxVRQghiqMwUE0UFBRg4sSJGDx4MLKzsznv39TUFM7Ozpz3q61at26N+fPns243a9Ys1gsYEUII32gCYTXw7NkzDBw4EJGRkbydo1mzZggPD+etf21UWlqK9u3bs16zoUmTJrh16xb09fV5qowQQtihkQEtd/bsWTRt2pTXIAAADRo04LV/bSQWixEcHAxTU1NW7SIjI7FgwQKeqiKEEPYoDGgpqVSKxYsXo1evXsjKyuL9fPXr1+f9HNqoTp062LhxI+t2q1evRmhoKPcFEUKIAigMaKHMzEz06tULS5YsUdnsdAoDVRs9ejQGDRrEqg3DMBg5cqRKghwhhHwKhQEtExERgaZNm+LcuXNK9cN2JjyFgaoJBAIEBQXBycmJVbvk5GRMnjyZHjckhKgdhQEt8ueff6JNmzZ4/vy5Uv14eHhgw4YNch+vr69f7TciUlaNGjWwe/du1gsyHTx4EHv27OGpKkIIkQ+FAS1QWFiICRMmYPz48SgqKlKqr7FjxyIiIgKlpaVyt/H09IRYLFbqvLrAz88Ps2bNYt1u6tSpePr0KQ8VEUKIfCgMaLjnz5+jXbt22LZtm1L9WFpa4uDBg/jzzz9hamrK6ll3ukUgv2XLlsHHx4dVm7dv3yIwMJBVQCOEEC5RGNBg586dQ9OmTXHv3j2l+mnVqhWioqIwePBg2fcoDPDDwMAAwcHBMDQ0ZNXu5s2bWL58OU9VEULIx1EY0EBSqRRLly6Fv78/MjMzlerr66+/xpUrV1C7du1y34+Li5O7DwoD7DRo0ABr1qxh3W7p0qWsFzCqSmFhIfbs2YNp06ahXbt28Pb2hr+/Px4/fsxJ/4SQ6oVWINQwb9++RUBAAE6dOqVUP0ZGRvjjjz8QGBhY4Wd5eXmsFsqJjo6Gt7e3UvXoGoZh0Lt3b5w5c4ZVO3d3d0RGRsLMzEyp8/fq1Qtnz56Fp6cnmjVrBmtraxw9ehR9+vTBpk2blOqbEFL90MiABvnvv//Qtm1bpYNA3bp1cevWrUqDQNl52PZH2BEIBNi+fTtsbW1ZtUtISMD06dOVPv/t27exfPlyxMXFYf369diwYQM8PDxoXQNCSKUoDGiIW7duoUWLFnjw4IFS/fTp0wfh4eEf/STP5tHEmjVrwtjYWKmadJWdnR22b9/Out2OHTtw5MgRpc5do0YNbN26FQ4ODjSqQwj5JAoDGuDAgQPo1KkTXr9+rXAfQqEQy5cvx7Fjx2BpafnRY9mMDLi6uipcEwF69+6Nr776inW7CRMmICUlReHzbt26FW3btqX/f4QQuVAYUCOGYbB48WIMHz5cqfUDbGxscP78efzwww8QCj/9v5TNyMCHEw8Je2vWrEG9evVYtcnKysLo0aMhlUoVOmfHjh2xe/du+Pv7K9SeEKJbaCUZNSkoKMCYMWPw119/KdVPixYtcPjwYbi4uMjdhs3IAIUB5RkbGyM4OBitWrVCSUmJ3O3++ecffPvtt/D390d2djays7ORk5ODwsJClJSUoKSkBMXFxR/984dPjZw9exYNGzascLxUKoW+vj709PRk/3z/z2x+9v4/zc3NYWlpCSsrK1haWsq+rKysWD9+SQjhD4UBNUhNTUXfvn1x584dpfqZPHkyfvnlFxgYGLBqx2ZkgIaZ2WMYBtnZ2Xj9+rXsIp6dnY3evXvj2LFjrPrauHGjQrsivs/e3l7257JaNIGBgUGFgPD+36sKEdbW1rCysmK99DMhpGoUBlQsOjoaX3zxBZKSkhTuw9DQEEFBQRg1apRC7WlkQHHFxcV4+fIlUlJSKv168eIFUlJSUFBQoO5SNV5RURFevXqFV69esW5raGgIJyenj345ODhAX1+fh8oJqX4oDKjQqVOnMHz4cOTl5SncR506dXDkyBHWS96WKSwsRGpqqtzH68rIAMMwyMzMrHBR//ArLS1N3aUSvPs9TkhIQEJCwkePs7W1/WRooFEGQigMqATDMFi3bh3mzJmj1Ha1nTp1wuHDh2Ftba1wH4mJiayOr04jAxKJBImJiYiPj8fjx49l/3zy5AmSk5NRWFio7hI55eXlhT59+sDc3BwAMGzYMDRr1gxhYWG4ceOGmqtTjbS0NKSlpSEqKqrKYwwNDeHs7Ax3d3d4enrCw8MDnp6e8PT0RK1atVhv902INqIwwLPi4mJMmTIFf/75p1L9jBs3Dr///rvSw55s5gtYWVkpvRKeqjEMg5cvX1a44MfHx+PJkycoLi5Wd4kqY2Njg7Zt2wIATpw4ATs7O9jZ2bEOhNVdYWEhnjx5gidPnuD8+fPlfqavry8LCWVfZWHB3t6eRhRItUFhgEdZWVkYMGAAQkNDFe5DIBBg9erVmDlzJidvPNVljYGMjIwKF/uyfypzG6Y6CQsLQ1hYmLrL0GrFxcWIjY2tdGMvU1PTCiMJZX+2srJSQ7WEKI7CAE9SUlLQo0cP/Pvvvwr3YWJign379qFPnz6c1aVtawy8efMG9+/fR1RUFKKiovDw4UPEx8crvYETIcrKzc1FREQEIiIiKvzMxsYGnp6eqF+/Pnx8fODj44PGjRtr3Ugb0R0UBngQFxeHHj16KDUc6+LiglOnTqFx48YcVsZuzoAqRwbKhvejoqIQGRkpu/g/efJEZTUQwpX09HSkp6dXmJtRt25dNGnSBD4+PrJ/Ojg4qKlKQv4PhQGO3b59G7169UJGRobCfbRo0QInTpwo93w4V9g8xlWrVi3Ozw+8m8gXHx9f7qIfGRlJM/VJtVc2N+HQoUOy79nZ2clGD8oCgoeHh1yriRLCFQoDHDp37hwGDhyI/Px8hfsYOnQoduzYASMjIw4r+z9s9j/gIozk5+fjwYMH5S769+/fp+fwCfn/Xr16hfPnz5ebvGhiYgJvb+9yowgNGzakVRsJbygMcGTPnj0YO3YsSktLFe5j0aJFWLRoEa8zlNl8+q5ZsyarviUSCR4+fCh7dC0iIgKPHj1SeH198mn9+/eHm5tbhaWCq1o6uKo/CwSCcksUV7a0Mdvv5efnIycnp9wqjNnZ2ZBIJOr+z6bx8vLycPPmTdy8eVP2PZFIJJuD0Lp1a7Rt2xYNGzakRx8JJwSMMg++EwDA2rVrMXv2bIXbGxgYYMeOHRg+fDiHVVXEMAz09fXlDizR0dEf3f42NzcXt2/fls1av3XrFt68ecNVudXCp5bXLfuysLDAunXrcPXqVVb9u7i4IDo6WmtmrzMMg9zc3AoBISsrq8L3Kvt+Tk6OUmt1VDfm5uZo1aoV2rZti3bt2qFly5YwMTFRd1lEC1EYUIJUKsV3332HNWvWKNxHzZo1ceLECbRq1YrDyiqXlZWFGjVqyH38y5cvy90qSE5Oll34w8LCEB0drbOf8oyMjCpdzc7R0VHh5XAzMjLg7e2NFy9esKpl2LBh2Ldvn048815SUoLU1NQql4Mu+1LmVp02E4lE8PHxQdu2bWVfTk5O6i6LaAEKAwoqKSnB+PHjsXv3boX7aNSoEU6dOqWyR/geP34MLy8vuY+/e/duuU/+bNYo0FYCgQB2dnYfvcg7OTnBwsKCl4vvxYsX0a1bN9bt9uzZg8DAQM7r0UYMwyAnJ6fSPSPe/3r16pVOjDK4urqWCwefffYZ3VogFVAYUEBeXh6GDBmCM2fOKNxHly5dcPToUdlSsapw/fp1tG/fXq5jBQJBtX2jNDIygoeHh2yBmLI/u7q6wt7eHmKxeqfSzJo1C+vWrWPVxszMDNHR0XBzc+OpquqnbJQhMTERT548wePHj8stYFVdJ7laWFjI5hy0bdsWLVq0oFsLhMIAWxkZGejVqxdu376tcB9DhgzB7t27WW89rKyjR49i4MCBKj2nuojFYtSpU6fCynCenp5wdHTU6Me2ioqK0KJFC9y/f59Vu7Zt2yI0NFTtYaY6kEqlePHihSwYvB8UEhISlJoorGnEYjGaNGmCDh06oHv37mjfvj1vTzMRzUVhgIXExET06NEDcXFxCvcxdepUbNiwQeUXI4ZhsHDhQixbtkyl5+WTQCBArVq1Kl0S1tXVVasviv/++y+aNWvGevOkH3/8EfPnz+epKgIApaWleP78eaVBITExUetH1AwMDGTBoHv37mjUqJFOzEfRdRQG5BQTE4Pu3bsjJSVF4T6WLVuGefPmqeyFlZqaipCQEFy4cAEhISEK7RuvKRwdHWXPWzdu3BgNGjSAu7t7tX7ueuPGjZg2bRqrNiKRCDdu3ECLFi14qop8TEFBARISEhAXF4fo6GjZ2hrKvG+om52dnSwYdO3alZfF0Ij6URiQQ2RkJLp166bwqoJCoRBbtmzBhAkTOK6svIKCAly/fh0XLlzAhQsXWA8zawKBQAAvLy/ZYitlX2zXPKgOGIaBv78/zp07x6pd3bp1ERkZCVNTU54qI2yVbaP8/uJb2roGR+PGjdGtWzd0794d7dq1o1sK1US1CgMMw8gWQCl7kQkEAohEIhgYGCg0NH/r1i307NkTOTk5CtVkaGiI/fv3o1+/fgq1/xiGYfDgwQPZxf/atWush5XVycjICN7e3uWWYm3YsCFNZnpPamoqGjVqhPT0dFbtxo0bh23btvFUFeHC+6tzli3NrW2rcxoaGpa7pdCwYUOlRj4ZhsHOnTsxZswYDqtUjlQqRVFRESQSiewWkFAolC3cVV1uoWh1GGAYBvn5+cjLy0NBQQHy8vI+mrQNDQ1hYmICIyMjmJqafvKecmhoKL744gvk5uYqVJ+FhQVOnTol9wx+eZQN/Zd9paamctY3n2xsbCps0OLh4aHV9/VV5dSpUwrtXHn06FH079+fh4oIXyQSCR4/flwuIERGRrIOg+ri4OAgGzXo2rUr7OzsWLWPiopCkyZNsHTpUixYsICnKj+utLQUubm5smvKxz5giUQiGBsbw8jICCYmJjA2NtbacKCVYUAqlSInJwdpaWkoLi5WqA+BQIAaNWrA2tq60oVhzp07h/79+yv8SdvBwQHnz59Ho0aNFGpfhmEYREdH4/jx4zh+/Diio6OV6k8V9PX10bx5c7Rt2xZt2rRBs2bN4OjoqLUvEk0wefJkBAUFsWpTo0YNPHjwAI6OjjxVRVSBYRi8ePECERERuHHjBsLCwnDnzh0UFRWpu7RP8vHxQe/evdGnTx80bdr0k6OzixYtwtKlSwEA8+fPx9KlS1X2vlFcXIz09HRkZWUpPAlUX18ftra2sLCw0OgnliqjVWFAKpUiIyMD6enpnK58Z25ujpo1a8omox0/fhxDhgxBSUmJQv15enri/PnzCm8BLJFIEBYWhmPHjuH48eN4/vy5Qv2oio2NTblFTZo2baryxyaru7y8PPj6+uLx48es2nXt2hXnz5/Xujcm8nFFRUWIiIgotyKopu/66eDggC+++AJ9+vSBn59fpXMNvL298eDBA9nfv/vuO6xYsYLXQFBYWIjXr19zupS6SCSCjY0NrK2ttea1pzVhoKioCImJibymYQcHB1y4cAEjR45UOGy0aNECp0+fhq2tLat2hYWFuHjxIo4dO4aTJ09q9LCgl5dXuYu/p6cnfepXgXv37qFVq1asn3Fft24dZsyYwVNVRBMwDIMnT57g+vXrsnCgzCPQfDM2Nkb37t3Rp08f9O7dG7a2tkhISEDdunUrHDtz5kysWbOG8/cYhmGQmZmJly9fctrv+wwMDFCrVi2t+HCk8WGAYRhkZ2fjxYsXKnl+98GDBxg7dqxCtwd69OiBw4cPyz2LOzs7G2fOnMGxY8dw9uxZ5OXlsT4n394f8i8b9rexsVF3WTpr5cqV+OGHH1i10dfXR3h4+Ec3nSLVT3p6Om7evCkLCOHh4QrfVuWTQCBA69atYWVlhb///rvSY6ZOnYpff/2Vs0BQWlqKlJQUvH37lpP+PkYgEMDR0RGWlpYa/aFJo8MAwzBISUlBdna2Ss97+/ZtTJkyhdULZ8SIEdixY8cnN6Z58eIFTp48iWPHjuHy5csK34rg23fffYcvvvgCTZs2rdbP8msbiUQCPz8/1rsbfvbZZwgPD6fHwHRYUVER7t27V270QNHHpdVh0qRJ+P3335Uedi8sLMTz589VvoqkpaUlnJycNDYQaGwYYBgGycnJCj/Sp6zr169j2rRpcl2sP/VL+vjxY9n9/1u3bnFdKi/S0tJoBEBDJSYmwtvbm/VrY9q0adiwYQNPVRFtwzAMHj16hIsXL+LChQu4fPmywk9OqcqYMWOwdetWhTdaKiwsxNOnT9W2voOFhQWcnZ01MhBoZBgomz2blZWl1jouX76MmTNnfjRBzpgxA2vXri33P5dhGNy7d08WAGJiYlRRLqcKCgpoRECDHThwAMOHD2fd7ty5c+jRowcPFRFtV1xcjFu3bsnWLbl7965GLq08cuRI7Nixg3UgKC4uRkJCgtq3XbeystLIp6s0Mgykp6drzPPzly5dwowZMypNku8/+sIwDO7fv48DBw7gwIEDGvUEgLOzM1q2bIkjR47IdbxIJEJJSYnG/bKS8kaOHIm9e/eyamNvb4/79++znuBKdE9GRgb++ecfWThISkpSd0kyAQEB2LVrl9yBQCKR4OnTpxrzOKa9vb3GjbxqXBgoW9tbk1y+fBnTp08vl5KXL1+OH374AfHx8Thw4AD279+P2NhYNVb5f4yNjdG5c2fZqmBeXl6IioqCr6+vXO0tLCxUPk+DsJeTk4PGjRvjv//+Y9WuT58+OH78OIU9IreyWwplweDy5cvIz89Xa02jR4/G9u3b5ZpDkJycrHHvae7u7ho1h0ejwgDDMEhISNDIJXVv3LiByZMng2EYLF68GKampti/fz/u3bun7tIgEAjQtGlTdO/eHd26dUPr1q0rPMpy7do1dOjQQa7+nJyckJyczEephGPXr19Hx44dWd8DDQoKwsSJE3mqilR3RUVFuHnzpiwcREREqOWWwrhx4/DHH398NBDk5eXh2bNnKqxKPoaGhnB3d9eYUK5RYSAjI4PXZz6VYWBggNWrV+Py5ct48uSJusuBk5MTevToge7du6NLly6fHHI6e/Ys/P395erby8tLo59RJuUtWLCA9dbUxsbGiIyMhKenJ09VEV2SlpZW7paCKndpnDRpEjZv3lzpRbVs/QVNuT3wIUdHR9SoUUPdZQAANGZh+NLSUo3eYreoqAjm5uZqDQINGjRAv3790L9/fzRt2pRVomQzS5h2u9MuCxcuxIULF3Dnzh252+Tn52PEiBG4cePGJx+HJeRTbG1tMWzYMAwbNgwMwyAqKgonT57EyZMnERERweu5g4KCIBaLsXHjxgrviRkZGRobBIB3e82Ym5trxB4tGrNOYlpamsZv59mpUyeVn7NVq1ZYuXIlHj16hH///Rc//fQTmjVrxnpoiU0YoF0DtYuenh727t3L+v/bvXv3sHjxYn6KIjpLIBCgSZMmWLRoEe7du4fExET8/vvv6NmzJ2/Bc9OmTZgxY0a5WxVSqVSjP2AC72rUlGWkNSIMSKVStT9GKA9XV1f4+Pjweg6xWIzu3btj8+bNSElJwc2bN/Hdd98pPZxLIwPVm4eHh0JrCKxcuZL1AkaEsOHi4oKvvvoKZ8+eRXp6Og4fPoxRo0ZxPjy+YcMGzJkzRxYIcnJyNPLRyA8pszESlzQiDOTm5mr8qECZ3r17c96niYkJBg0ahODgYKSlpeH8+fOYPHkyp7vNsdkj3djYmLPzEtUZO3Ys6y2LGYbByJEjNW6mNamezMzMMHDgQOzatQsvX76EhYUFp/2vXbsWP/zwAxiG0YoPmMC7D8OqWBb5UzQiDGRmZqq7BLl16dKFk12obG1tMW7cOJw6dQrp6ek4dOgQRowYAUtLS+WLrASbsKWnp8dLDYRfAoEAW7duhYODA6t2iYmJ+Prrr3mqipDK3blzh5cVZn/++WcsWbJE7Y8+sqEJ10C1z1ooLS3V+CUw32djY4P27dvjypUrrNu6urqif//+6NevH9q2bavwkpqKYLPqlrZsuUkqsra2xq5du9C9e3dW7fbt2wd/f38EBATwVBkh5R07dkyp9vb29qhTpw7q1KkDNze3cn8Wi8Vate9Cbm4uSktL1TqRUO1hQJvSW5kvvvhC7jDg6emJoUOHYsCAAWjcuLHanillMzJAYUC7devWDTNmzMD69etZtZsyZQratm0LV1dXfgojBO9uTUmlUrnCQO3atVGvXj14eXmhbt26sou+q6vrR29nPn36lMuSVSI/Px/m5uZqO7/awwDbBYYWLlwIV1dXjB079pPHzps3TzZ5hUsdOnSAkZFRlffhXVxcMGzYMAwfPhw+Pj4asagEhQHdsnz5cly8eBEPHjyQu82bN28watQoXL58WaWjVkT3eHp6yhYC0tfXh5eXF+rXr4969erJvjw9PRV6solhGKUXrpNKpZg+fTpatWpVbrTs3r17WLVqFWbMmIFWrVopdY4PFRYWqjUMqP1dn83ENjakUin+++8/1KlTh/O+jYyM8MUXX5T7nq2tLb7++mtcu3YNz58/x6pVq9CkSRONCAIAhQFdY2hoiH379lVYifJTrl27hp9//pmnqgh5N7fl/WV4bW1t0blzZ0ydOhWLFi3C8OHD0aRJE4UfcS4tLVV6QrpQKMSAAQNw7tw55OXlAXg32rBu3ToEBgZyHgQA/q6F8lL7uz6bBLdx40b8+++/+PvvvzFw4EAMHDgQr1+/rvTYFy9eoLCwkJcwAAADBw6EhYUFxowZg/Pnz+PFixf47bff0K5dO428mFIY0D0NGzZU6MK+aNEihIeH81ARIe94e3vL/pySkoJff/0VHTp0gLOzM7755htcuXJF4d0FuVrOvkOHDjA1NcXZs2eRlpaG5cuXo3PnzhU+CHJF3cvwq/U2gVQqRUlJidzHjx07Fi9fvpQNwwOocljl6dOnEIlEqF27Nie1fqh9+/ZITU3Vmm1+KQzopm+++QZnz57F+fPn5W5TWlqKgIAARERE0JoThBdeXl6Vfv/ly5f47bff8Ntvv8He3h4DBgzAoEGD0KFDB7lvXXF1URWJROjfvz/279+P69evo27dunLdnlZUSUkJpFKp2t5/1fquzzb5mZiYQCwWw8DAAFZWVrCysqryF+Tp06dwdnaudMWrvLw8zJ07F7NmzcK3336LkJAQ1rULhUKtCQIAu//WdL+4+hAKhdixYwfr7VLj4+Mxc+ZMnqoiuq6qMPC+1NRU/P777/Dz80OtWrUwe/ZsREVFfXKBHkVHFCrTvn17FBYWQiAQYMaMGRUu1Hfv3sU333yDr7/+GhcvXlT6fFzWzpZawwAXqy5dvXoVAQEBsq+YmBgA78JAVbcIDA0N8eOPP2Lt2rVYuXIljh49ynrRB21ZJKkMjQzoLgcHB2zbto11u61btyr9+BchZXJzc3H37l3s2bOH1UgV8O6279q1a9GkSRM0bNgQK1asqHLrbi5X8yt73bx586bC/C+JRIKdO3di8eLFWLNmDU6cOKH04kHqXIlQrbcJuPgXb968OTw8PGR/L1vi8vnz51VO8hCJRLJPvyUlJWAYRiOWg+RDcXExIiIiWG21TGGg+unbty8mTpyIP/74g1W7CRMmoGXLlpyuhkmqt+zsbDx8+BAxMTGIjY1FbGwsYmJikJSUxEn/MTExmDdvHubNm4f27dsjMDAQgwYN4nx54/379yMiIgIrVqzAkiVL8M8//+Dzzz+X/Tw+Ph4uLi6wtrYGADRp0gRRUVFo3769wufU2TCgyEx7sVhc7lOukZFRuZmpwLvhpby8vI9OHszLy8OCBQvw8uVLjBo1ivUjHZrylMDH5Ofnw9fXF48ePYK+vj68vb0hEokQGRn50XYUBqqndevWITQ0FI8fP5a7TUZGBsaMGYOzZ8/S7wUph2EYJCcnIyoqCpGRkbJ/Pn/+XGU1XLt2DdeuXcPUqVPRq1cvBAYGynZ0VebCGhISgpMnT2LJkiVwdXVFr169cPz4cXTr1k22MFBWVla5AFKjRg2lVxJU52tMrWFAkX9xW1tbxMfH4/Xr1zA0NISpqWmFfsoWnBAKhUhMTJR9XywWyz7hmJiYYN26dcjOzsaqVavQunVrVksBa0MYCA8Px6NHj3Dq1Cl0794d+vr6SE9Ph62t7Ufb0Zt+9WRiYoLg4GC0bt0apaWlcre7cOECNm7ciOnTp/NYHdFkEokEjx8/RmRkZLkLv6as8ldSUoLjx48jLi4Ov/zyC5ycnBTu6969e9i2bRtmzpwp2yDu888/x4kTJ3DlyhV06dKFq7IrUOd1Ra1hQCQSQSgUsrqf3bdvX9kbU3FxMTZv3oyaNWuWO6YsDPzwww/lvl+/fn0sW7as3PcsLS3h6uqK2NhYtG7dWu462D6/rQ5lewwcOXIEa9asQfv27eV6Q6cwUH01a9YMS5Yswf/+9z9W7b777jv4+fmhUaNGPFVGNEV+fj4ePnxY7sJ///59tT8H/zEikQjfffcdFixYgMLCQiQnJyvUT0JCAtatW4eRI0eiZcuWsu+bmJjA398fx44dQ6dOnSASiWBlZVVuJCAzM7PcLWu2hEKhWidvq/02gaGhIasliR0dHbFixYqPHhMYGIjAwMAqf56dnQ0DAwMYGRkhLy8PMTEx6NGjh9w1ANCKJwmaNWuGQYMG4ebNm0hISEDjxo3laqfOGa2Ef9999x3OnTuHa9euyd2mqKgII0aMQHh4uFb87hP5MAyDhIQE3LhxA2FhYbhx4wZiYmK0aoK0t7c3duzYAV9fX6X7cnd3R3BwcKU/Gz58OIYPHy77u4eHBxITE5GRkQFjY2NERkZi8ODBCp/b0NBQd0cGALAOA1xIS0vDli1bZBMH/f39Wa9HoA0jA/r6+jh06BAAVBg9+ZiioiK+SiIaQCQSYc+ePWjcuDGrXeMePnyIH374gfWeB0RzlE0oDgsLk138X716pe6yFKKnp4f58+fj+++/L/cIeWWPk/NBJBLhyy+/xKJFi8AwDPr27QszMzOF+/tw7puqaUQYUDUPDw+sXbtW4fYGBgZaMWfgfWzqVfdKWIR/tWvXxu+//856l8JffvkFn3/+OetdEYl6ZGRkyD71h4WFITw8vFqE/WbNmmH79u2V3rYSCoUwMDBQyb9n8+bN0bx5c076UveIm9rDgLrTkCK0sWYKA+RDI0aMwN9//419+/axajd69Gg8ePCA9UJGhF8Mw+Dx48eyC39YWBgePXqk7rI4ZWBggKVLl2LmzJkf3e7XyMhI60KPzocBQ0ND6Ovro7i4WN2lyI3NUweags2kQG17ERHFbdq0CWFhYVUu4FKZ1NRUTJgwAUePHtW6EbLqJjk5GSEhIQgJCcHFixeRlpam7pJ406ZNG2zfvl2u1QstLCyQnZ3Nf1Ec0dfXpzAgEAhgZWWlNfetxGKxwrtpqcPVq1exZ88eVi8MGhnQHZaWltizZw86derEatLY8ePH8eeff2L8+PE8Vkc+lJubi9DQUFkAiI2NVXdJvNPT08OPP/6I2bNnyz3b3tTUFGKxmNUjtOpkZWWl9mCt9jAAvHtD0pYwoAn/0+RVUlKCAQMGwNbWFk2bNoW7uzv09PTQrl07AO+ep63scSEaGdAt7du3x/fff4/ly5ezajd9+nR06NBB9iw24Z5EIsG9e/cQEhKCCxcu4ObNm6w2d9NETk5OqF+/PurXr49Hjx7hwoULVR7boEED7N27F02aNGF1jrIPmdoyUqIJo80CRkPW4X3+/Dlyc3PVXcYneXh4aMWTBACQk5MDS0tL/Pnnn+jdu3eFn7dr1w7x8fEVvt+mTRuEhYWpokSiIUpKStCmTRvcvXuXVbvmzZsjLCxMtqYFUd6zZ89kn/z/+ecfZGVlqbskhbi6uqJRo0ayC3/9+vVRr149WFhYyI7ZuXMnxowZU2n76dOnY8WKFQrP0SoqKqr0/U3TmJqawtXVVd1laMbIAADY2NhofBgwMzPTmiAAvNve2cfHB+PGjWPVjm4T6B49PT0EBwejSZMmrB71DQ8Px5IlSyos5kXkV1BQgIsXL+Ls2bMICQnBkydP1F0SK2KxGA0aNICPjw+aNGkCHx8f+Pj4yPVpt7L7/46Ojti5cye6deumVF0GBgYwMzNTevMgvmnKRFyNGRkAgMTERLx580bdZVRKIBDAw8NDZc+wcqWgoAChoaG4ceOG3G/Yn332GR4+fMhzZUQTbd26FRMnTmTVRigU4sqVK7LbT+TTMjMz8ffff+P48eM4d+6cytdaUZSJiQkaN26MJk2ayC78n332mcKT3zIzM2Ub/QDA4MGDsWXLFs42HSouLkZ8fLzGbkRnYWEBFxcXdZcBQMPCQElJCR4/fqyR/+Ps7Ow+uaa/JgsJCZH72XB3d3et+3RCuMEwDPr3748TJ06wale7dm1ER0eXGwIm5SUlJeHEiRM4fvw4QkNDNX6lz5o1a8ou+mUX/rp163K+XLmtrS2Ki4vx22+/ITAwkPM5WWlpaRo5J00gEMDT01NjbrFpzG0C4N1QpZ2dHVJTU9VdSjn6+vrl0qs2YpPcaQKh7hIIBNi2bRtu377N6nX433//4euvv8bevXt5rE67MAyDmJgYHD9+HMeOHWO1jbiqCYVCNG7cGG3btkXbtm3Rpk0b1KpVSyXnHj9+PCZPnsx6FVh5WVtbIysrS+MeX7e3t9eYIABo2MgA8O4F9PTpU43aFKNOnTowNjZWdxlKCQ8PR4sWLeQ61sbGRmtm4RJ+nD9/Hj179mTdbt++feXWb9c1EokEt27dwvHjx3H8+HGNHWEzMzND69at0aZNG7Rt2xYtW7ZUaildTZefny/bwE4TGBkZoU6dOhr1ZJrGhQEAKC0tRUJCgkY8QuPs7KwRj30o6/79+3JvVGRsbIy8vDyeKyKa7ttvv8WGDRtYtbGwsEB0dDRvn/I0UXFxMS5evIjjx4/j5MmTGjkkXbt2bdmn/rZt26Jhw4Zq3SFPHbKyspCSkqLuMqCnpwd3d/ePrqCoDhoZBoB3Q9VPnz5V6301bZ8n8L5nz56hTp06ch9fXFysUUNYRPUKCwvRvHlz1pNJ27dvj8uXL1friw3DMLh58yb27t2LgwcPIiMjQ90lyQiFQjRp0gTt2rWTDfk7OTmpuyyNoO75AyKRCHXq1NHIp9I0NgwA6g0ENWvWZLXTn6YrW3NAXq9evapW//5EMQ8ePEDz5s1ZzyNZvnw5fvjhB56qUp+4uDgEBwdj3759GjXs7Obmhu7du6Nbt27w8/ODlZWVukvSWK9fv8br169Vfl5NDgKAhocB4N0n1KSkJJXNIRAIBHBycqoWtwbeJ5VKoaenJ/eSs7GxsahXrx7PVRFt8Msvv2DGjBms2ojFYty8eRPNmjXjqSrVSU1NxYEDB7B3716NmQRoYWEBPz8/WQBwd3dXd0laJTs7GykpKSp7cs3Y2BjOzs4a/Wi6xocB4N2Q3OvXr3mf1GZoaAgXFxeNTW7Ksra2RmZmplzH3rhxA61bt+a5IqINpFIpevbsiZCQEFbtPD09ERERoVV7eZR5+/Ytjh8/jr179+LixYus9m3gg0gkQuvWrdGtWzd069YNzZs317h7ztqmqKgISUlJvC+yVrNmTdja2mrUZMHKaEUYKJOXl4fk5GReJhba2NigZs2anD9Dq0k8PDzknt18+vRp9OrVi+eKiLZ48eIFvL29Wd8bnzhxIoKCgniqilslJSW4cOECgoODcfz4cbU/0eTp6Sn75N+pUyeYm5urtZ7qSCqV4vXr10hPT+e8bz09PTg7O2tNGNaqaGliYgJPT0+8efMG6enpSr9YhUIhrK2tYW1trRMpm819RG1dD53ww9HREVu3bsWAAQNYtfvjjz/g7++Pvn378lSZ8iIiIrBjxw4cOHCAl4uCvPT09ODn54e+ffvC399fp57IUBehUAh7e3vZqGlGRobSo0BGRkawsbGBubm5xo8GvE/rroACgQAWFhawsLBAfn4+MjIykJeXJ/dWlQKBAEZGRrCwsICVlVW1Hgn4EIUBooz+/ftj/Pjx2LZtG6t248ePR8uWLWFvb89TZezl5eXhwIEDCAoKQnh4uNrqMDU1hb+/P/r16wd/f39awVFNyha8s7W1RVZWFnJyclBQUCD3nIKyre2tra21dk0arQsD7zM2Npb9hy8pKUFBQQHy8/NRUlIChmHAMAyEQiGEQiGMjIxgZGQEQ0NDrUprXGKz3re8cwuIblm/fj1CQ0NZLaaTnp6OL7/8EmfOnFF7+L5//z6CgoKwd+9ete2DYmdnh759+6Jfv37w8/OrtnOUtNH7o8UMw6CwsBAFBQUoKCiAVCqFVCqFQCCAQCCAnp4ejI2NYWRkVC0ew9bqMPA+PT096Onp0X21j6CRAaIsU1NTBAcHo02bNqwe+T1//jx+++03TJs2jcfqKldQUIBDhw5hy5YtuHnzpsrPDwB169ZF//790a9fP7Rs2bJar8FQXZSNIiu6hbK2qTZhgHwamzBAIwOkKi1atMCSJUswf/58Vu3mzp0LPz8/NGzYkKfKyouNjUVQUBB2796tlnDbrFkz9OvXD/369UODBg10dkSSaAcKAzqEzW0CGhkgH/P999/j3LlzuH79utxtioqKEBAQgDt37vA2NF5UVIQjR44gKCgIV69e5eUcH9OsWTMEBARg4MCBGrM1LSHyoDCgQ+g2AeGKSCTCnj170LhxY1b33u/fv4958+Zh7dq1nNbz+PFj/PHHH9i5c6fKlwZ2c3NDYGAgAgIC4OXlpdJzE8IVCgM6hG4TEC65urri999/R2BgIKt269atQ8+ePdGtWzelzs8wDC5fvoy1a9fizJkzSvXFlrW1NYYOHYqAgAC0bt2abgEQradViw4R5Vy5cgWdOnWS61grKysKBEQuI0aMwP79+1m1cXR0xP3792Ftbc36fCUlJTh48CDWrl2LyMhI1u0VZWhoiL59+yIgIAA9evTQ6KVlCWGLwoAOefToEav9BvLz83VmJi1RXHZ2Nho3bozExERW7fr3748jR47I/ak6JycHW7duxYYNG5CcnKxIqawJBAJ06dIFAQEBGDBgAD2tRKotCgM6JDc3F2ZmZnIfHx8fj7p16/JYEakurly5gs6dO7Pe+GXz5s2YPHnyR49JTEzEhg0bsHXrVrx9+1aZMuXm4+ODwMBADBs2jLb/JTqBwoCOsbS0RE5OjlzHhoaGomPHjjxXRKqLefPmYcWKFR89xsDAAA0bNoSpqSnCwsIgEonw4MEDeHh4VDj23r17WLt2LQ4ePKiSbcxNTU0RGBiISZMmwcfHh/fzEaJJdGctXgIArD7lpKSk8FgJqW4WL16Mpk2bVvnzoKAgvH37Fnfv3kVoaCjMzMxQVFSEwMBA2eZjUqkUp0+fRufOndGsWTPs37+f9yDg6+uLoKAgvHjxAps3b6YgQHQSPU2gY5ycnBATEyPXsaq6L0uqB319fQQHB8PX1xf5+fkVfn7y5Ens2bMHDg4OOHjwoOz7d+7cwaJFi+Dm5oZ169YhLi6O91qNjY0xfPhwTJ48Gc2aNeP9fIRoOgoDOoZGBgifvLy8sG7dukrnAfz9998AgJ49e1b42aduL3ClUaNGmDx5MgICAmhTIELeQ7cJdAyFAcK3iRMnok+fPuouQ8bQ0BCjR4/GjRs3EB0djSlTplAQIOQDNDKgY5ydneU+lsIAUYRAIMC2bdvg7e2N1NRUtdVRv359TJo0CaNGjWK14BYhuohGBnQMjQwQVbC1tcWOHTtUfl6BQIA+ffrg8uXL+PfffzF9+nQKAoTIgcKAjmETBl6+fAmpVMpjNaQ669mzJ7766iuVnMvQ0BCTJk1CbGwsTpw4gU6dOtESwYSwQLcJdAybMFBaWorXr1/D3t6ex4pIdVRQUICgoCAcPXqU1/PY2Nhg6tSpmDJlCmxtbXk9FyHVGYUBHWNraws9PT3Zc92fkpycTGGAyK0sBPz8888V5gt4eHjAzc1NthZBly5d8ObNG0RGRiItLY3VeTw9PTFr1iyMHDmSlswmhAO0AqEOql27ttzryB85cgQDBgzguSKi7QoLC7Fly5ZKQ0CZOXPmYMqUKRW+P3HiRISEhMh1no4dO2LWrFno1asXhEK6y0kIV2hkQAc5OzvLHQaePHnCczVEm0mlUhw4cADz5s3Df//999FjV69ejdWrV7M+h0gkwuDBgzFr1ixaIIgQnlC01kHu7u5yH0thgFTlypUraNmyJQICAj4ZBJQxbNgw7N+/n4IAITyiMKCDKtsUpirx8fE8VkK0UWxsLPr06YNOnTrh7t27vJ8vODgYp06d4v08hOgyCgM6iMIAUcSrV6/w1VdfoVGjRiq/OI8bN06tCxgRUt1RGNBBbMJASkpKpZvOEN2Rl5eHH3/8EXXr1sWWLVtUsp3wh9LS0jBmzBjQfGdC+EFhQAfVrVuX1fE0b0A3SSQS/Pnnn/D09MTChQuRm5ur1nrOnTuHTZs2qbUGQqorCgM6yMLCgtUCLXSrQLcwDINz586hSZMmGD9+PF68eMFp/xYWFli0aBF++OEH1m3nzJmDf//9l9N6CCH0aKHO8vDwkHuhFxoZ0B1RUVGYM2cOLl68yHnf5ubmmDFjBr799ltYWlqitLQUV65cwY0bN+Tuo7CwECNGjMCdO3dgYGDAeY2E6CoaGdBRNImQvC8zMxNfffUVfH19OQ8C5ubmWLRoEZ4/f47FixfD0tISACAWi7F3716YmZmx6u/+/fv43//+x2mNhOg6CgM6isIAAd4tGrRjxw54eXlhy5YtnE7QMzExwYIFC2QhoLLdA93c3BSaB7B27Vr8888/XJRJCAGFAZ1FYYBER0ejffv2GDt2LNLT0znrVygUYuLEiXjy5AmWLl36yS2EAwMDMXToUNbnGT16NDIyMhQtkxDyHgoDOopNGHj58qXaZ5IT7uTk5ODbb7+Fr68vq/v18ujduzcePHiAoKAguTe4EggE2Lx5M1xcXFidKyUlBZMmTaLHDQnhAIUBHUWPF+oehmEQHByMevXqYcOGDZBKpZz17evri0uXLuHUqVNo0KAB6/ZWVlbYvXs3BAIBq3ZHjhzBzp07WZ+PEFIehQEdZWZmBjs7O7mPf/jwIY/VEL7FxMTAz88PgYGBnK7kV6tWLezduxfh4eHo3LmzUn116tQJc+fOZd1u2rRpSEhIUOrchOg6CgM6zMvLS+5j79+/z2MlhC+5ubmYO3cuGjdujNDQUM76tbCwwM8//4xHjx4hICCAs+2Ely5dCl9fX1ZtcnNzERAQgJKSEk5qIEQXURjQYY0aNZL72OjoaB4rIVxjGAaHDx9G/fr1sXr1apSWlnLSr1gsxrRp0/DkyRPMnTsXhoaGnPRbRl9fH8HBwTAyMmLV7vbt21i2bBmntRCiSygM6LDGjRvLfSyFAe0RHx+Pnj17YvDgwUhOTuas30GDBiE2NhYbNmyAjY0NZ/1+qF69eli3bh3rdsuWLeN8QiQhuoLCgA5jEwZevXqFV69e8VgNUVZpaSl+/vlnNGrUCBcuXOCs35YtWyIsLAyHDh1iPfFUUZMmTcIXX3zBqo1UKkVgYCDevHnDU1WEVF8UBnTYZ599xmr2Ns0b0FwPHz5EmzZt8P3336OoqIiTPq2trbFt2zbcuHEDbdq04aRPeQkEAmzbtg01a9Zk1e7Zs2eYNm0aT1URUn1RGNBhJiYmrNYboFsFmqekpAQ//fQTfH19ER4ezkmfAoEAEydOxKNHjzBu3DjOJgeyVbNmTYUeG9y1axcOHjzIfUGEVGMUBnQcm1sFNDKgWaKjo9GyZUvMnz+fs5n0vr6+uHXrFoKCgmBtbc1Jn8r4/PPPMXXqVNbtJk2ahKSkJB4qIqR6ojCg47y9veU+lkYGNENxcTEWL16MZs2aITIykpM+LS0tsWnTJty5cwctWrTgpE+urFq1ivVCRtnZ2Rg1ahQkEglPVRFSvVAY0HFsRgZiY2NRXFzMYzXkUyIiItC8eXMsWbKEs8cFv/zySzx69AhTpkyBSCTipE8uGRkZITg4GHp6eqzahYaGYu3atTxVRUj1QmFAx7EJAyUlJYiLi+OxGlKVoqIi/O9//0OLFi04u13j7e2Na9euYceOHawn6qmaj48PVqxYwbrd/PnzERERwUNFhFQvFAZ0nIuLi2x/eXnQrQLVu3PnDnx9fbF8+XJOhr3NzMywfv163Lt3D+3ateOgQtWYMWMGunTpwqpNSUkJAgICkJ+fz1NVhFQPFAZ0nEAgYDVvgCYRqk5BQQHmzp2L1q1bIyYmhpM+hw8fjri4OHz77bcQi8Wc9KkqQqEQO3fu/OSWyB+Ki4vDnDlzeKqKkOqBwgBhdavg3r17PFZCyoSHh6NJkyZYvXo1J7sL1qlTBxcvXsS+ffvg6OjIQYXq4ezsjK1bt7Ju9/vvv+P06dM8VERI9UBhgLAaGQgPD6cZ2jySSCRYuXIl2rRpg0ePHindn0AgwPTp03H//n3WQ+yaauDAgRgzZgzrdmPHjqVVNAmpAoUBwmqXuNzcXPz77788VqO7kpOT0a1bN/zwww+cPCng4eGBq1ev4pdffoGJiQkHFWqODRs2wN3dnVWbtLQ0jB07FgzD8FQVIdqLwgBBo0aNWO0Sd+vWLR6r0U3Hjh1D48aNcfnyZaX7EgqFmD17NqKjo7VqgiAbZmZm2Lt3L+tHIc+cOYPNmzfzVBUh2ovCAIGenh6aNWsm9/E3b97ksRrdkp+fj8mTJ2PAgAHIzMxUur969eohLCwMq1evZr0NsLZp1aoVFi5cyLrdrFmzOJuQSUh1QWGAAHj3xiovGhngRlRUFJo2bYqgoCCl+xIKhfj+++8RGRnJ6v+ltps3bx7rTZQKCwsREBDA2YZOhFQHFAYIAKB169ZyHxsXF8fJp1hdJZVKsX79erRs2ZKTRZwaNmyI27dvY8WKFTA0NOSgQu0hFouxZ88emJmZsWoXFRWFBQsW8FQVIdqHwgAB8G7Pejbu3LnDUyXVW2pqKvz9/TFz5kyll3YWi8VYsGAB7t69y+o2T3VTp04dbNy4kXW7NWvW4NKlSzxURIj2oTBAAACOjo6oVauW3MfTrQL2zpw5A29vb5w/f17pvho3bozw8HAsXboUBgYGHFSn3UaNGoUhQ4awasMwDEaNGkWjXISAwgB5D5tbBTSJUH6FhYWYPn06evXqhbS0NKX6EovFWLJkCcLDw+Hj48NNgdWAQCDAli1b4OzszKpdSkoKJk+eTI8bEp1HYYDIsJl4dvv2bU5WxqvuYmNj0aJFC/z6669K9+Xu7o6wsDAsXLiQ9Q5+usDKygq7d++GQCBg1e7QoUPYvXs3T1URoh0oDBAZNmEgJyeHdjD8hIMHD6J58+Z48OCB0n2NGjUKkZGRaNGiBQeVVV+dO3fG7NmzWbebOnUqEhISeKiIEO1AYYDINGnSBPr6+nIfT/MGKldSUoKZM2di6NChyMvLU6ovc3NzBAcHY9euXaxnzOuqH3/8EU2aNGHVJjc3FyNHjuRk5UdCtBGFASJjYGDAamniGzdu8FiNdnr58iW6dOmC9evXK91XmzZtEBUVhREjRnBQme4wMDBAcHAw68csb968iZ9++omnqgjRbBQGSDlsbhXQY1nlXb9+Hb6+vrh27ZpS/QiFQixatAhXrlyBm5sbR9Xplvr162Pt2rWs2/344480OZboJAoDpBw2YeDZs2d4+vQpj9VoB4Zh8Msvv6Bz585ITU1Vqq9atWrhypUrWLx4McRiMUcV6qavvvoKvXr1YtVGIpEgMDAQb9++5akqQjQThQFSDtuNbUJCQniqRDvk5uZi+PDhmDFjhtL3m4cMGVKtNxdSNYFAgO3bt6NmzZqs2j19+hTTpk3jqSpCNBOFAVKOk5MTvLy85D5el8NAXFwcWrRogb/++kupfkxMTLB9+3YcOHAAlpaW3BRHAAA1a9bEjh07WLfbuXMnDh8+zENFhGgmCgOkAj8/P7mPvXTpEiQSCY/VaKYjR46gefPmiI2NVaqfZs2aITIyEmPGjGH9fDyRj7+/P6ZMmcK63cSJE5GcnMxDRYRoHgoDpAI2YSArKwsRERE8VqNZSktLMWfOHAwaNAi5ublK9TVnzhyEhYXBw8ODo+pIVVavXo369euzapOVlYXRo0fT4lpEJ1AYIBV06tSJ1fEXL17kpxAN8+rVK3Tt2hVr1qxRqh9zc3McO3YMq1atYrWuA1GcsbEx9u3bx3rlxkuXLmHdunU8VUWI5qAwQCqwsbFB48aN5T5eF+YN3LlzB76+vrhy5YpS/TRs2BB3795Fv379uCmMyM3Hx0ehdQTmzZuHqKgo7gsiRINQGCCVYnOrICwsDPn5+TxWo15//fUXOnbsiBcvXijVT0BAAG7dukW3BdRo1qxZ6Ny5M6s2JSUlGDFiRLX+HSeEwgCpVJcuXeQ+tri4WOmFdjQRwzBYsmQJhg0bhsLCQoX7EYvF+O2337Bnzx6YmJhwWCFhSygUYteuXbCysmLVLjY2FnPnzuWpKkLUj8IAqVT79u0hEonkPr663SooKChAQEAAFi9erFQ/jo6OuHr1Kr7++mt6WkBDuLi4ICgoiHW7TZs24cyZMzxURIj6URgglTI3N0fz5s3lPr46hYHU1FR07twZ+/fvV6qfTp06ISIiAq1bt+aoMsKVwYMHY/To0azbjRkzBq9fv+ahIkLUi8IAqRKbeQP379/Hq1eveKxGNe7fv4+WLVvi9u3bSvUzd+5chISEwM7OjqPKCNd+/fVX1KlTh1Wb169fY9y4cWAYhqeqCFEPCgOkSmzCAAD8888/PFWiGqdOnULbtm2RmJiocB9mZmY4evQofv75Z9pbQMOZm5tj7969rG6HAcDp06exZcsWnqoiRD0oDJAqtWnTBgYGBnIfr63rDTAMg3Xr1qFv375KLST02Wef4e7du+jfvz+H1RE+tW7dGvPnz2fdbtasWYiLi+OhIkLUg8IAqZKRkRHatGkj9/EhISFaN3xaXFyMiRMnYtasWUrVPnz4cNy6dQuenp4cVkdUYf78+ax26wT+b4JpcXExT1URoloUBshH9ejRQ+5jk5OT8ejRIx6r4VZmZiZ69OiBbdu2KdyHQCDAqlWrEBwcDFNTUw6rI6oiFouxd+9e1v//IiIisHDhQp6qIkS1KAyQj+rduzer47XlVsHjx4/RqlUrhIaGKtyHiYkJjh8/jjlz5tBjg1rO3d0dGzduZN1u1apVSv0OEaIpKAyQj2rQoAFcXV3lPl4bHjG8dOkSWrZsifj4eIX7cHFxwfXr19GnTx8OKyPqNHr0aAwaNIhVG4ZhMHLkSGRlZfFUFSGqQWGAfJRAIGA1OnD58mWUlJTwWJFy9u/fj549eyI7O1vhPlq0aIE7d+7Ax8eHs7qI+gkEAgQFBcHJyYlVu+TkZEyePFnr5ssQ8j4KA+STevXqJfexb9++xdWrV3msRnEbNmzAiBEjlAorQ4YMQWhoKOzt7TmsjGiKGjVqYPfu3axv+xw8eBB79uzhqSpC+EdhgHxSp06dYGxsLPfxx44d47Ea9hiGwbx58/Dtt98q1c/ChQuxf/9+GBkZcVMY0Uh+fn6YNWsW63ZTp07Fs2fPeKiIEP4JGBrbInLo168fTpw4IdexTk5OSExMhFCo/qxZWlqKSZMmYfv27Qr3YWBggO3bt2PEiBEcVkY0WVFREVq1asV66+I2bdrgypUrtOAU0Trqf7cmWoHNvIGUlBTcu3ePx2rkk5+fjwEDBigVBGrWrInLly9TENAxBgYGCA4OhqGhIat2N27cwIoVK3iqihD+UBggcvH392d1vLpvFWRmZqJ79+44deqUwn00atQId+7coY2GdFSDBg2wZs0a1u2WLFmCW7du8VARIfyh2wREbs2aNZP7E3+9evUQGxvLc0WVS05ORs+ePfHvv/8q3EevXr2wf/9+mJmZcVgZ0TYMw6B3796sty52d3dHZGQk/f4QrUEjA0RubG4VxMXFqWXt9tjYWLRp00apIDBz5kycOHGC3sgJBAIBtm/fDltbW1btEhISlJ6wSogqURggcmO7GqGqbxXcvn0b7dq1Q1JSkkLtBQIBNm3ahLVr17LeyY5UX3Z2dgrNO9m+fTuOHDnCQ0WEcI9uExC5SaVSODk5ITU1Va7jW7Rogdu3b/Nc1Ttnz57FoEGDkJ+fr1B7fX19BAcHs16BjuiOKVOmYPPmzazaWFlZ4cGDB6wXMiJE1WhkgMhNKBSyWoDozp07SElJ4bGid3bv3o0+ffooHATMzc1x7tw5CgLko9asWYN69eqxapOVlYXRo0dDKpXyVBUh3KAwQFjp168fq+OPHz/OSx1l1qxZg9GjR6O0tFSh9nZ2drhy5Qo6d+7McWWkujE2NkZwcDD09PRYtfvnn3/wyy+/8FMUIRyhMEBY6datG6uJdXzNG2AYBgsWLMCcOXMU7sPd3R03btygPQaI3Hx9fbFs2TLW7X744QdER0fzUBEh3KA5A4S1ESNGYP/+/XIdKxKJ8Pr1a1haWoJhGEilUggEAgiFQggEAoW2/mUYBrNmzcL69etZty3j6+uLM2fOwM7OTuE+iG6SSCTo2rUr662LGzRogLt37350OWuGYWSvE4ZhZK8TTVjNk1RvFAYIa0ePHsXAgQM/edxnn30Gf39/DBo0qNK9DUQiEYyNjWVfRkZGn3zTk0ql+Prrr7FlyxaF6+/SpQuOHj0Kc3Nzhfsgui0pKQne3t6sd7/85ptv8Ouvv8r+LpVKUVBQgPz8fNmXRCKp0E5PTw8mJiYwMjKCsbExDA0NFQrShFSFwgBhLT8/H7a2tpVO2GvQoAG6du2Krl27ws3NjVW/AoEANWrUgLW1NfT19Sv8vLS0FOPGjcPu3bsVrn3IkCHYvXs3DAwMFO6DEAD466+/MGzYMNbtzpw5gy5duiAjIwOZmZkKbX0sFothY2MDKysregyWcILCAFHIoEGDZM9Ql40AtG/fnnUAqIqFhQVsbGxkQ6rFxcUIDAzEoUOHFO5z6tSp2LBhAw25Es6MHj2aVTg1NTXFypUr0bFjR07OXxagbWxsWE9sJOR9FAaIQs6fP4/bt2+jR48eMDU1VejTjTxsbW1hbm6OIUOG4PTp0wr3s2zZMsybN4+GVgmn3rx5Ax8fH7m2Lm7atCl+/PFHuLi4cF6HQCCAk5MTLC0tOe+b6AYKA0QhEokEL1++ZH3PVBFZWVkYMWIEkpOTWbcVCoUICgrC+PHjeaiMECAsLAwdOnSoci0BoVCIqVOnYsyYMbxvbWxpaQlHR0ca/SKs0W8MYa2wsBAJCQkqCQLAu1Xc/vjjD9jb27NqZ2BggKNHj1IQILxq27Yt5s+fX+nPXF1dsWfPHkyYMIH3IAAA2dnZePLkCQoLC3k/F6leaGSAsFJYWIhnz55VOuOZb0+fPsX48eORlpb2yWNNTExw+vRpdOrUif/CiM4rLS1Fu3btyi2/PWLECHzzzTcwNTVVeT0ikQh16tShibJEbhQGiNyKi4uRkJCgliBQJj4+HuPHj0dmZmaVx1hYWODs2bNo3bq1Cisjuu7Jkyfw8fGBnp4efv31VzRp0kSt9YhEIri7u1f6ZA4hH6IwQOQikUiQkJCA4uJidZeCR48eYcKECcjKyqrwM2tra1y4cAG+vr5qqIzourNnz6Ju3booKSnRiP0I9PX14e7uTo8fkk+iMEDkkpiYiDdv3qi7DJm4uDiMGzeuXE12dna4ePEiGjZsqMbKiC6TSqWIj49HSUmJukuRMTc3R61atdRdBtFwNIGQfNLbt281KggAQL169bB//37Z/VhnZ2dcvXqVggBRq7S0NI0KAsC7xx/fvn2r7jKIhqMwQD5KKpXixYsX6i6jUrVq1cKJEyfg7e2Na9euwdPTU90lER1WVFQk1+RWdXjx4oVG3LYgmotuE5CPevXqlca+wQHvnhqoVasW3RMlasUwDJ4/f468vDx1l1IlW1tb2piLVIlGBkiVSkpKNDoIAEBeXl6leyQQokq5ubkaHQQAzbyFQTQHhQFSJVUtKqSsjz1mSIgqaMvvoLa8ponqURgglWIYRmve4N6+fYvS0lJ1l0F0VGlpqdZM0FN0l0RS/VEYIJUqKCjQqiFF+sRD1EWbfvdKSkpQUFCg7jKIBqIwQCpV2YI+mkzb6iXVA8MwWve7p231EtWgMEAqpS3DnmWKioq0aiSDVA+lpaUoKipSdxmsaNtrm6gG/9toEa1TWlrK6h78woUL4erqirFjx8p1fEJCAk6cOIGYmBjk5ubC2toanp6e6N+/v1IrpRUWFkJPT0/h9oSwpYrdAbl+vZSWlkIikdDjuKQcGhkgFfD5BhcSEoLvv/8exsbGmDt3Ln799VdMmjQJBQUFuHbtmlJ907atRNX4/p3j6/VCrxXyIVp0iFSQnp6O1NRUuY7duHEjQkNDy31v8+bNqFmzZoVjY2NjsXDhQowbNw49e/as8PO3b9/CzMxMoZqBd7sVuri4KNyeELaSkpKQk5Mj17E3b97EwYMHkZqaCn19fbi5ueH777+HoaFhpcfz+XpxcHCAtbW1Qm1J9US3CUgFbD41jB07Fi9fvoSLiwuGDRsG4N3GKJXZuXMnGjRoUOkbGwClggAAmiVNVE7e37msrCysX78eI0eORMuWLVFQUIDY2NiPPubH5+uFXivkQxQGSAVs5guYmJhALBbDwMAAVlZWVR6XnJyMJ0+eYPbs2XL1W1RUhGnTpqFNmzYYPXq0XG0kEolcxxHCFXlfK1lZWZBIJGjZsqVs1Kx27dpVHi/v62Xy5MkwMjKCUCiEiYkJli5dKlc99FohH6IwQCpQ9s7R1atXERQUJPv7//73P6SnpwMA3N3d5erj8OHDrDceojteRNXk/Z2rXbs2GjVqhJkzZ8LHxweNGzdG69atYWpqqvTrZfny5TAyMmJVN21aRD5EYYBUoOxFtXnz5vDw8JD9vUaNGkhJSQGAKu+Pvu/FixdISUlBs2bNkJSUJPd5KQwQTSUSibBo0SI8evQIUVFROHPmDPbt24eVK1cq/XpRBL1WyIfoaQJSgUAgYHW8WCwu90nDyMgIDg4Osi8DAwPZI1CxsbGV9vH+s9q7d+9GYGAg73UToiw2v3MCgQD16tXDsGHDsGbNGojFYty+fVup14tAIMDChQsxd+5cXL16lZe6iW6gkQFSgVDILiPa2toiPj4er1+/hqGhIUxNTSv04eXlhcaNG+OPP/5AQUEBvLy8wDAMnjx5gvPnz2Py5MlwcXHBnTt34ODgAEdHR8TFxbGqg97giKrJ+zv3+PFjPHjwAI0bN4aFhQXi4+Px5s0bODs7V3q8vK+XZcuWwdraGllZWVi8eDFq1aoFV1fXT9bD9jVOqj8KA6QCtgv39O3bFxs3bsT06dNRXFxc5aOF33//PU6fPo0TJ07g1atX0NPTg4ODA5o1awYnJycA7940w8LCcPPmTRQWFkIikcDIyAhDhgzhvG5ClKWnpyfXZDxjY2PExMTg9OnTKCgogK2tLUaPHg1fX98q28jzeil7PNDKygq+vr54+vSpXGGAXivkQ7TOAKkgMzMTL168UHcZuHTpEpKSkuR+msDKykr2JkmIKqSkpKhtrf/CwkIwDAMjIyMUFBRg4cKFmDRpEurWrfvJto6OjqhRo4YKqiTagkYGSAV8TVrim7bWTbSXOn/nsrOzsWrVKgDvng7o2rWrXEEAoNcKqYhGBkgFUqkUMTEx6i6DNTc3N5iYmKi7DKJD8vLy8OzZM3WXwVqDBg1o3gAph34bSAVCoRD6+vrqLoM1+rRDVE0bf+f09fUpCJAK6DeCVKqqJYU1lbGxMe3CRlROJBLB2NhY3WWwom2vbaIaFAZIpT62tLAmoslQRF207bWibfUS1aAwQCplYGDAeolTdREIBPRph6iNhYWF1qxxYWRkBAMDA3WXQTQQhQFSJW35BGFpaUn3QInaCIVCWFpaqrsMuWjLa5qoHr2DkipZWFhoxUWWbhEQddOG30GhUAgLCwt1l0E0lOa/0xO1EYlEcHBwUHcZH2VlZaU1tzNI9WVkZKTxn7odHBxoki2pEoUB8lGWlpYae7EViUSwt7dXdxmEAADs7Ow0diTNyMhIa25lEPXQzN9cojEEAoHGLvFrb29Pn3SIxhCLxRo7kubk5KQ1kxyJelAYIJ9kaGgIGxsbdZdRjomJCX3SIRrH0tJS49YdsLGx0crFkYhqURggcrGzs4Opqam6ywDwbsc1FxcX+qRDNI5AIECtWrU0ZldAU1NT2NnZqbsMogUoDBC5CAQCuLi4qP0ThkgkgqurK8Ri2mOLaCaxWAxXV1e138IyNDSk0EzkRmGAyE0kEsHNzU1ti5ao+/yEyMvAwABubm5qm1BYdn51BxKiPWjXQsKaRCJBcnIy3r59q7JzGhgYoFatWhQEiFYpKipCYmIiioqKVHZOMzMzODs7UxAgrFAYIAphGAaZmZl4+fIl7+eytrbW6Me2CPkYqVSKV69eISMjg/dzOTg4oEaNGnRrgLBGYYAopbCwEImJiSguLua8b6FQCGdnZ9p3gFQLb968QXJyMqRSKed96+vro1atWmqf00O0F4UBojSGYfDmzRukpaWhsLBQ6f7EYjFsbGxgZWVFQ52kWpFIJMjKykJ6ejpKS0uV7s/Q0BC2trYwNzen0QCiFAoDhDMMwyA/Px9paWnIy8sD218tIyMj2NjY0BsbqfYYhkFOTg4yMjJQUFDAqq1AIICJiQlsbW1hbGxMrxXCCQoDhBdSqRRFRUXIz89Hfn4+CgsLIZVKIZVKIRQKIRAIYGBgAGNjYxgbG8PQ0JBGAYhOkkgkKCgoQEFBAfLz81FUVASGYWSvFaFQCENDQ9lrxcDAgObPEM5RGCCEEEJ0HMVLQgghRMdRGCCEEEJ0HIUBQgghRMdRGCCEEEJ0HIUBQgghRMdRGCCEEEJ0HIUBQgghRMdRGCCEEEJ0HIUBQgghRMdRGCCEEEJ0HIUBQgghRMdRGCCEEEJ0HIUBQgghRMdRGCCEEEJ0HIUBQgghRMdRGCCEEEJ0HIUBQgghRMdRGCCEEEJ0HIUBQgghRMdRGCCEEEJ0HIUBQgghRMdRGCCEEEJ0HIUBQgghRMdRGCCEEEJ0HIUBQgghRMf9P0jlT2lppwhCAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gt_graph = toys.links_to_graph(links, tau_max=tau_max)\n",
    "\n",
    "sys_var_names = ['$X_%s$' % str(i) for i in system_indices]\n",
    "temp_context_names = ['t-$L_%s$' % str(i) for i in latent_indices_time] + \\\n",
    "                     ['t-$C_%s$' % str(i) for i in observed_indices_time]\n",
    "\n",
    "spatial_context_names = ['s-$L_%s$' % str(i) for i in latent_indices_space] \\\n",
    "                        + ['s-$C_%s$' % str(i) for i in observed_indices_space]\n",
    "                            \n",
    "tp.plot_graph(gt_graph, var_names=sys_var_names+temp_context_names+spatial_context_names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f10ed4c",
   "metadata": {},
   "source": [
    "The corresponding ground truth time series graph looks like this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "780b1c2c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-04T07:29:27.133937320Z",
     "start_time": "2023-12-04T07:29:25.547337027Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tp.plot_time_series_graph(gt_graph, \n",
    "                          var_names=sys_var_names+temp_context_names+spatial_context_names, \n",
    "                          node_classification=node_classification)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d30eaf83",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-04T07:29:27.191361180Z",
     "start_time": "2023-12-04T07:29:27.145137998Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Set up a suitable dummy and dataframe depending on which test one wants to use\n",
    "# We start with the dummy for a variant of ParCorrMult: \n",
    "# For that, add one-hot-encoding of time-steps and dataset index to the observational data. \n",
    "# These are then the values of the time and space dummy variables.\n",
    "dummy_data_time = np.identity(T)\n",
    "\n",
    "data_dict = {}\n",
    "for i in range(nb_domains):\n",
    "    dummy_data_space = np.zeros((T, nb_domains))\n",
    "    dummy_data_space[:, i] = 1.\n",
    "    data_dict[i] = np.hstack((data_observed[i], dummy_data_time, dummy_data_space))\n",
    "\n",
    "# Define vector-valued variables including dummy variables as well as observed (system and context) variables\n",
    "nb_observed_context_nodes = len(observed_indices_time) + len(observed_indices_space)\n",
    "N = len(system_indices)\n",
    "process_vars = system_indices\n",
    "observed_temporal_context_nodes = list(range(N, N + len(observed_indices_time)))\n",
    "observed_spatial_context_nodes = list(range(N + len(observed_indices_time), \n",
    "                                            N + len(observed_indices_time) + len(observed_indices_space)))\n",
    "time_dummy_index = N + nb_observed_context_nodes\n",
    "space_dummy_index = N + nb_observed_context_nodes + 1\n",
    "time_dummy = list(range(time_dummy_index, time_dummy_index + T))\n",
    "space_dummy = list(range(time_dummy_index + T, time_dummy_index + T + nb_domains))\n",
    "\n",
    "vector_vars = {i: [(i, 0)] for i in process_vars + observed_temporal_context_nodes + observed_spatial_context_nodes}\n",
    "vector_vars[time_dummy_index] = [(i, 0) for i in time_dummy]\n",
    "vector_vars[space_dummy_index] = [(i, 0) for i in space_dummy]\n",
    "\n",
    "# Name all the variables and initialize the dataframe object\n",
    "# Be careful to use analysis_mode = 'multiple'\n",
    "sys_var_names = ['$X_%s$' % str(i) for i in process_vars]\n",
    "context_var_names = ['t-$C_%s$' % str(i) for i in observed_indices_time] + ['s-$C_%s$' % str(i) for i in observed_indices_space]\n",
    "var_names = sys_var_names + context_var_names + ['t-dummy', 's-dummy']\n",
    "\n",
    "dataframe = pp.DataFrame(\n",
    "    data=data_dict,\n",
    "    vector_vars = vector_vars,\n",
    "    analysis_mode = 'multiple',\n",
    "    var_names = var_names\n",
    "    )\n",
    "\n",
    "# Now we set up the dummy and dataframe that are suitable for RegressionCI, RegressionCI does the one-hot-encoding under the hood: \n",
    "dummy_data_time_regr = np.linspace(0,T,T, endpoint=False).reshape((T,1))\n",
    "\n",
    "data_dict_regr = {}\n",
    "for i in range(nb_domains):\n",
    "    dummy_data_space_regr = np.array([i for j in range(T)]).reshape((T,1))\n",
    "    data_dict_regr[i] = np.hstack((data_observed[i], dummy_data_time_regr, dummy_data_space_regr))\n",
    "\n",
    "# We also need to specify the data_type for RegressionCI, 0 is continuous and 1 discrete data\n",
    "data_type =  np.zeros((50, 100, 7), dtype='int')\n",
    "data_type[:, :, 5] = 1 # space context\n",
    "data_type[:, :, time_dummy_index:] = 1 # dummies\n",
    "\n",
    "dataframe_regr = pp.DataFrame(\n",
    "    data=data_dict_regr,\n",
    "    analysis_mode = 'multiple',\n",
    "    data_type=data_type,\n",
    "    var_names = var_names\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8462d8e4",
   "metadata": {},
   "source": [
    "Let's look at the first two datasets by plotting the time series with the function ```tp.plot_timeseries```. Note that the observed spatial context variable ```s-C_5``` is constant across time but has a different value within each dataset, and the temporal context variable ```t-C_3``` has the same values in each dataset but it varies over time. \n",
    "\n",
    "We are not plotting the values of the dummy variables since these are very high-dimensional and not informative since they are just labelling the time steps and datasets, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aff51ed0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-04T07:29:28.345715377Z",
     "start_time": "2023-12-04T07:29:27.186832818Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tClWG+Ph4GBkZ5ehKkd3oExUVVaTP9b3379+Dy+UW+GCZPCUSyLZp00bucBsfPnxQWiAbFhaGjRs3AvgWYA4bNky2LCQkRDbk1p49exTuu6RoIAt8a17ncrm4detWrvUyMzOxevVqNG7cGA0bNkSTJk3g5uaGFi1awMvLCz4+Phg+fDiWLFmCgwcP4r///itVw3rNnTsXHA4Hp06dglQqxcKFC0FE6NGjBzIzM3Ose/v2bdSoUQP6+vrFCoCkUik2b94MQ0NDWFpaFtiPR1kmTpwIgUCAR48eAfjfgXHLli0gIly9elXlZZBIJPjll1/Qpk0bpR2Qb9y4ASLCpk2b5C4/ePAgiChHEJmWloYePXqAiDBp0qRilUUsFqN+/fpwdXXNMzAXiUTo0qULBAIBjh07VuS8ilK24p78pFIp9u3bh5EjR2L58uU4ffo04uLi1Hpr+caNG2jVqhU4HA60tbXRq1cvnDhxAmKxGGKxGN7e3tDX18eDBw8KlW5iYiIOHjyIMWPGoFGjRkVuaSqOkydPIjg4WOHvV5kBjkgkgpWVFQIDA/Ndb/bs2dDR0SnWk+dfv37F8uXLUbduXRARnJycFPrMV69eld3pCAsLK3L+P8rKykL79u2hq6ub44GmuLi4HAGtkZERevTokW9azZo1K3QwVZCSCmQ7duyI2rVr59tda9myZbLb+zNmzCh2ntHR0eDz+fneoVuwYAF4PB7+++8/2Xvfn7MHDBiATZs2Fflctn79ehCR7Pjcp08f2NraKu045+XlBQ8Pj0JvVyKB7NmzZ+Hl5YXhw4dj9uzZmD17NoYNGwYvLy+cPXtWKXnMmTMHkZGRAIBnz57l2IkCAwNlt4VPnTqFP/74Q24aQqEQKSkpste7d+8KDGSFQiHEYjEyMjJQr149ODk5yd5LTU3FsmXLYGlpCS6Xi44dO2Lo0KEYMGAA+vTpA39/f3Tp0gXt2rWDnZ0dtLW1ZQcfDocDa2trtGvXDpMmTcLu3bvx7NkziEQi2YmoJF7nz58Hj8fDH3/8keP9HTt2QFNTE25ubvjw4QNEIhHCwsKgqakJBwcHPH78WCn5v3r1Cq1atQIRYd68eSr9/NevXwePx8Ps2bNl7wmFQty8eVNWv56enir/ziMjI0FEuHz5slLT9fHxwS+//IKMjIwc76elpcHW1haenp65vl+RSIT58+eDw+HAy8sLBw8eRHJycqHzzu4mUdBnSk9PR4cOHaClpZUj6FLlK7uOs/dbsViMY8eOoVGjRqhQoQL++OMPvHnzRu62IpEIR48ehaOjI4gI1apVg0AgkO3HRkZGcHFxwbRp00ps342JiUG/fv1ARKhXrx7WrVuHr1+/5lrv69evqF+/PqpVq4b4+Ph807x06RLGjx8PR0dHcLlcEBGsra1Rv359mJqaIjU1tVhlXrt2Lfr165frtynv9ejRIxgYGICIcPTo0SLXcVFf2fvntWvX8l3v9evX4HK5WL16daHzuHr1KgICAqCtrQ0+n48uXbrI+stHRkYWuH379u1Rp04d9OzZE5UqVUJSUlKxP3dWVhb69OkDDQ2NPL/3mJgYjBo1Cg0aNEBMTEy+6UVERIDD4eDFixfFLpsq6jmv1+PHj2UPbuW3XmZmJurXrw89PT0kJCQoJe8ZM2aAy+XKPY6+e/cO+vr6GDFihNxt//nnH/D5fHC5XNlIPIV9iUQieHp6okqVKnj9+jW0tLQwZ84cpX2369atA4fDwatXryAWK34xUmIze0kkErp37x4lJCQQEVGFChWofv36Spt9IywsjPT09Khv374UHR1NGzZskE03GxISQh06dCB3d3fau3cvvXr1isaNG5crjeJOUXvnzh0aNGgQjR49mrhcLm3atIm+fPlCbdu2pQEDBpCNjU2+n0EqldLnz58pJiaGYmNj6e3bt/T06VN68uQJff78mYiI9PX1qWbNmqSpqUkikYiysrJIJBKRSCQisVhMDRo0oK5du+Y5s1lhJCUlUc+ePcnCwoLWrFlDfD4/x/J79+7R77//TgYGBlSzZk06ffo0+fn50ZgxY5Q6ZzT+fy7zdevWUefOnSkkJCRXWYpLLBZT3759iYho06ZNctOPioqi8ePH05o1a8jR0VGp+X9v6NChJBQKacOGDUpN99mzZ9SjRw+aMmUKdezYUfb+5s2bacWKFbRt27Y8p0e8cuUKLViwgN69e0cCgYAcHByoadOm5OzsTL/88ku+s9BkZmZSp06dyMHBQaF5t7OysmjcuHF069YtWrFiBTVs2LDQn7WoHj16RCtWrKDr169T/fr1qVatWnTkyBESCoXUsmVL6t69OzVo0IA4HA7duXOHwsLC6NatW1S/fn0KDg4mR0dHEovFFBcXRy9fvqRXr17Rf//9R1FRUbR+/XqqX7++ysqelZVF27Zto/Xr15OGhgYFBQVRx44d891X4uPjqV+/flSlShUKCwuTTeWd7cOHD7R06VI6deoUmZqakqOjIzk5OZGjoyNZWlrSmzdvyM/Pj0JDQ6lt27ZFKveXL1+oY8eOlJ6eTt7e3jRt2rQ8f08ZGRkUEBBAWVlZpKmpSWZmZrRixYoi5VtUI0eOpMTERNq0aVOB644aNYoSExNp48aNCqUtEolo7NixdOXKFbKwsKBOnTqRj48PmZmZEQAaOnQoJScn09atW4nL5cpN4+nTp9SzZ0+aOXMmNWzYkLp06UKDBg2igQMHFupz/mjZsmW0efPmYtX199LT06lNmzbUt29fCgwMLHZ6JWXBggV0+vRpOnToEGlpaeW77vv37+nTp09Ur149peQtFotp4MCBlJKSQtu2bcuR/+zZs+ncuXO0b98+MjQ0lLv91atXacmSJTRnzhyqUaNGkcoQGxtL/v7+ZGpqSu/fv6cjR46QmZlZkdL6UWpqKnl5eVFQUBD16dNH4SlqVdIie+rUKVUkm6/bt2/L+m7MmTMnR9+byMhITJ06FcC31tm8+sgWp0U2+zVw4EAQEXg8Hvr376+0lsmYmBgcPHgQM2fOhJ+fH3x9fdGtWzf06dMHgwYNQnBwMIKDg2FlZQUigrOzMzZt2oS0tLQiX3l5e3vDxMREdnUk7/XkyRPUqlULenp62Lp1q8qugsViMcLDw8Hn89GmTRu5LUzFeYWGhoLH4+H69es53v/+Cl8kEqFRo0Zo2rSpylrXoqOjZX3HVJG+n58fqlativT0dIjFYsTFxcHAwADBwcEK/SYePnyIv/76C23atIGWlpbsIa39+/fnud3MmTOhoaGBp0+fKlzOlJQUtGjRAnp6erh06ZLC22VlZcHHxwfDhg1TuI6EQiH279+Pbt26gYhQu3Zt7Nu3T7b9ly9fsHTpUtmDab/++ivatGkDIkKDBg1w4MCBfPPKyspC1apVMXDgQJXUqVgsxr59+1C9enXw+XyMHDkSHz9+VHjbixcvQiAQoF+/frLPkZ6ejnnz5kFXVxcVKlTA+vXrkZWVJXd7Dw8PuLu7F7nsw4YNg6GhIZYtWybry5fX769Xr17Q0dHBnTt3ZLdI79y5o1AdF7elLiEhASNHjgSHw8HatWsV2mbPnj0gIty6dUuh9YOCgqChoYGdO3fKLeu5c+dkT4/nlUa3bt1gY2Mja90eOXIkDAwM8OHDhyJ/9vnz54OIsGTJEqX+bgMCAmBjY5Pnb6uwL2W1yB49ehRLly7F8ePH8fbtW9l+kZCQAB0dHUydOlWp30NhXg8ePICWlhaGDx8ue+/GjRvgcDhYtmxZiZRh0aJFICL4+PgoPe3OnTujYcOGEIsVb5FVSSBrZ2cn66+aF1X0HRs3bhzc3NzQs2dPCIVC2cxeIpEI/fr1g5ubG0aMGKFweoXpI5vt69evmDdvHl68eFH0D1IMIpEI+/btg6enJ4gIZmZmmDhxYqEH5166dCmICAcPHixw3dTUVHz48KGoRS6UU6dOyQaKVsasVZ8/f8aoUaPA5/MREhKSa7lYnLPPVfbYkQVNTFBUAQEBsLa2VmjQ7KJ4+PAhOByOrN9cYGAgjI2Ni/SkaHp6Ok6cOIH27duDiBASEpLrwZL4+Hjo6uri999/L3T6qampcHNzg6Ghody+5/IsWbJEdlt/8eLFCm0ze/Zs8Pl8VK5cGREREXl+9xKJBMeOHUO7du3g4OCA7du3KzSkGfDt4SA9Pb0iD/idF6lUikmTJoGI0K5dO1nf7sLavHkziAgLFy7EqVOnULt2bfB4PIwaNSrH0+jybNu2DURUpLyfPXsGPp8vmyEx+/b52rVrc627atWqHA8ICYVCWFpaYsCAAQXm8+N+XBhZWVlYtmwZjI2Noaenh7lz5yqcTlZWFmxsbFCrVq0Cj8EbNmxQaBaltm3bolatWnJ/p9njla5evVr2XkJCAvT09BTaB6VSKeLi4nDmzBmsXLkSwcHBaN68uewCQ9myH5r6sYuhVCrF7du3sXTp0hz9PQtSnHrOFhMTAx0dHXA4HNmxxMDAAE2aNIGrqys0NTVL7HyXl+yLvtOnT0MqlcLd3R116tQpsWltxWIxxo4di7t37yo97b1794KIClXvKglko6Ki4ODggNDQ0FzLxGIx9u7dizZt2qgia6UqSiBbmjx69AijRo2CgYEBrKysFP7R3bx5ExoaGqV2FIb79++jSpUqqFy5cpGHxMrMzMTixYthZGQEfX19zJs3T+5B4McDY/awMr/++qvSL8Y+fPgAgUCABQsWKDXdH/Xq1QuWlpa4evWq7Cq+OKRSKRYtWgQejwc3N7ccM9EMGTIExsbGsik8CyspKQmNGjWCtbV1gfOzP336FNra2hg5ciQmTJgALpdb4AXHzJkzZXObq3L4vNevX4PD4eCff/5RWpoikUh2B0gZg+9nB8T0/2M9K3q8yMzMhJmZWZGOF127doWVlZVsGCapVIphw4aBx+Ph8OHDsvWuXr0KDQ2NXA0R8+fPh0AgKDBILEqAI5VKcfjwYdSqVQscDgeDBg0q0vjWT58+hYWFBerWrZvnb/jGjRvQ1NTEwIEDCzyuZD+4Ka+xKCAgQO54pTNmzIBAIMCbN2/kpimRSDB9+nQYGxvLfgMaGhqws7ODn58fVqxYoZLGp+zhm3r37g2pVIp///0X48ePR/Xq1WXPinA4HHTq1Enu9Kc/yqueZ86cqfBQU/7+/qhYsSI+f/6Mp0+f4sCBA5g3bx769u0LJycnzJw5s0ifVZkkEglatmwJKysrREREKDSxQVmRkZEBAwMD2V10RajsYa8HDx7Azc0Nw4YNQ2ZmJoRCIbZu3YoWLVrA0dGx2CfPklDWA9lssbGxaNiwIfT09PI9sUulUmzYsAEGBgayh9ZKq7i4ODRq1Ah6eno4efKkwttJpVLs3r0b1atXB5fLRVBQUL5X1/IOjNm391q2bImePXti6NChCAkJwezZs7Fs2TLs3bsXDx48yDWiQ0FmzpwJHR2dIgd9inr69Cl4PB4MDAyUehV/6dIlWFpawszMDCdOnMDDhw/B5XKLPa/627dvYW5uLnsYTR6xWAxXV1f88ssvsgeP2rdvDwMDgzxbCufOnQsiwuzZs0tkX/b09IS7u3uB6wmFQuzatSvfYefS09Ph4+MDPp+f50gUhSWRSDBt2jRs2bKl0EHLuHHjYGJiUqjRVq5duwYiwoYNG3K8LxaL4evrCx0dHdy4cQMJCQmwsrJC06ZNcx2Tvnz5Al1dXUybNi3fvAobyH758kXWfaRFixbFHkP60aNHqFixIurXr5/r7kdCQgKsra3h5OSk8PfXsWNHVKtWLce+m994pcnJyTA3N5c7HFhycjJ8fX3B4XAwZswYREZG4vHjxyq7K/SjuXPnQlNTE9bW1iAimJubIzAwEMePH0dKSgoiIiJk3Xo8PT1lrZDyyKvn7HG0TU1NZVPJ5iUqKgpEpNQLTlV58+aN7KHH9u3bq7s4ShUQEFCo0RBUOmpBXFwcOnToAG9vb7i5ucHZ2RmrV68u1nhqJam8BLLAt6lHvb29weVy5Q7inV1X9P9jvxZ0O7E0SE1NxW+//QYNDQ2FprZ78uQJ3NzcQET47bff8PDhwwK3yesEuGDBAvj4+KB58+b49ddfYWtriwoVKkBTU1PWosHlcmVj+o0aNSrfWdEyMzNRsWJFlcx2I09AQECOYVSU5ePHj2jbti04HA5sbGxQvXr1Qgf08kRFRYHH42H8+PFyly9evBgcDgcXLlyQvZeUlIQ6deqgZs2auYLC7D5eM2bMUMrtSEVs3boVRISnT5/mu96cOXNARNDR0UFwcDAeP36cY/mXL1/g5uYGHR0dlXVxKawnT56ACjGNslQqhYeHB+zt7eV+72lpaXB2dkaFChXg6uqKChUq5Dnn/IgRI2BmZpbv4PqFqWORSAQvLy8YGxtj//79SmuJfPDgAczNzdGwYUPZ71EkEqFly5YwNzcv1Ox29+7dA4fDydENYfjw4TAxMcnz/Lp8+XJwudwcw629ePECdnZ20NfXx6FDh4r4yYonPj4eTZs2xbBhw3D27Fm5AbRYLMauXbvQsGFDEBEaN24st/uevHr29fWFra2tbPzkvLoDiUQi2Nvbw9nZWeEuQ+q2ZcsWmJiYFLlLUWl16tQpEJFCrfCACgPZlJQUrFmzBs7Ozqhfvz4aNGiQ64Bc2pWnQBb4tpOPGjUKRITRo0dDLBZDKpVi69atMDY2RsWKFUt8MPriysrKQt++fUFEWLp0qdx1smcZ09bWRo0aNQrVglvYIEcqleL9+/c4f/48wsPDMW7cOHh7e8Pa2hra2tp55r1x40YQUYntI1++fFHZtMoSiQRz584Fn89Xah5//fUXiCjX+MSPHz+GlpaW3Fn7nj17BmNjY3h5eclOkNn9aL8fILwk9uX09HQYGhpi0qRJea4TFxcHXV1dDBo0CDNmzECFChVkLS6nTp1CTEwM6tWrBxMTkxIZ07gwWrRogWbNmim07qFDh/IdUB74dlFUo0YN8Hi8fAdcf/78uewBrLwUpo5///138Hg8lTy0fPfuXZiYmMDJyQmJiYkYO3YseDxeoWZYyubv7w8rKytkZGTg/fv30NLSwqxZs/JcXygUolq1avDx8QHwbfYxExMT2NraFqo/ojpJpVIcP34c1tbWclshf6zn//77D0SEdevWySZkyWvc4+XLl4PD4RR6GnZ1K6mW85IkFotRsWJFjB07VqH1VRLILlmyBE5OTvD09MTOnTuRlpaGCRMmwMXFRSWdg69duwZnZ2c0a9YM/v7+uW6VRkVFwcrKCh4eHnlOKSdPeQtks61cuRJcLhfe3t7o1KkTiAj+/v5FeuCnNJBKpRg/fjyICBMnTszRghIfHy97GGno0KGFfthGWUFOeno6fvvtNwgEglwP0EmlUjg4OOC3334rVh6ljbKnn5RKpfD394eurq5sqkaxWAwXFxfY2trKnT0QAE6fPg0ej4cxY8Zg5cqVsgfTsn8nJRXIAkBQUBAqV66cZ14DBgyAqamprMUuIyMDGzZsQP369WX9Fq2trUtl4JE993xBZROJRKhbty5atGhRYGtnXFycQtO8durUCXXq1MmzJU3ROs6+oMzrolgZoqOjYWRkBFtbW9lIAEXx+PFjcLlcLF++HBMmTIC+vn6BsyBmT+wyfPhw8Hg8tGrVSuVdmVRh165dsoedvvdjPffv3x+Wlpayu0Jjx46FhoZGrodHExISYGRkVOAkF0zJ2blzJ/bv36/QuioJZNu2bSsbtPl7S5YsgYODQ64fX3HFxcXJTpoTJ07E7t27cyyPiopSOLL/XnkNZAHgyJEj0NPTg5mZWa7vq6xavHix7MEdkUiE/fv3w8zMDBUqVMjx4EhhKDPIEQqF6NKlC/h8Pnbs2CF7P7vPbWFain9WqampsLe3R40aNfD161f8+eef4HA4uHjxYr7bLV++XNblY8yYMTkCqJIMZLMf1JHXJSA6OlruvOjAtyD+7NmzGDVqlFJG61CF7Ie+Ro8ene962Q+n3LhxQ2l5Zz/9nldXC0Xq+Nq1a9DU1ERAQIDKZ2S7fv06DA0NZQ85FVX//v1RsWJF6OvrY8KECQWuL5FIZBdFo0aNKrOteVKpFC4uLmjQoEGOOv2+nt++fQs+n59j9JLMzEw4ODigdu3aOS58Bw0aBCMjowIfKGVKJ5UEsvntmDt37kS9evWwefNmVWSNadOm5ZrLPSoqCjY2NnBzcyvUlXZ5DmSBb8OMlMWr8fxs3rwZfD5f9nCAr69vsQ5Oyg5yRCIRevfuDS6Xi/Xr1wP49uBG3bp11TqdaVny/PlzGBkZwd3dHVpaWgo9LS+VSjFlyhRMnTo11/dckoGsVCqFvb09/Pz8cr3v7u6OunXrltngAgDGjx8PY2PjPFvj09LSULlyZXTv3l2p+UqlUtldQHkKquN3797BwsICLi4uSunTrYjk5ORi7/MvX74En8+HlpaWwkMsPn78uMgX9qVJ9kNc3z8s+H09jxkzBsbGxrlGI3n06BG0tbUxZMgQAN8uKjgcDlasWFGSxWeUqMRm9vre+fPnacyYMXTr1i2lpvvmzRvy9/enCxcukIaGhuz91NRU2Yw1vr6+FBoaKnfGiKysLMrKypL9n5ycTJaWlvnO7GVvb6+02ckY5Th+/DiNHTuWfv/9dxowYEC+s04VRCKR0P3795Vaz1KplIYPH05r166lkJAQWrRoEa1evZoGDRqklPR/BseOHSMfHx+ytbWl6Oho0tHRKXJaqqjj/CxbtowmTpxIMTExshlx9u3bR926daOjR4+Sl5eXysugKs+ePaM6derQxo0bqVevXjmWffr0iSZOnEhbt26lBw8e0C+//KLUvHfu3Em9evWi6OhoatCgQY5l+dVxZmYmtWjRguLi4ujatWtUqVIlpZZL1ZYvXy6bye1n06NHD7p8+TI9fvyYdHR0ZPVcuXJlqlGjBo0aNYpmzZqVa7u1a9dScHAw7dmzhxYsWEBCoZCuX7+u9BkjmeJR9HislkCWiOjhw4dkZ2dXqG3ev39P/v7+ud7fsWMH6ejoUIcOHSg8PDzf6VnDwsJIS0uLBgwYkGtZcaeoZRhFAaAlS5bQtm3byNDQkI4cOVLgdIdMTpcuXaIqVapQlSpV1F2UQvn69Su1a9eORo0aRT169CChUEjdunUjGxsbWrZsmbqLV2xBQUEkEokoIiKCiL4FsFu2bKE9e/YQh8Oh4cOHU/fu3ZWer1gspo4dO5KjoyPNmDFDoW0A0IwZM+j06dMUHh5OdevWVXq5GNWJjY2lrl270sCBA3M0BISHh9M///xDhw8fJmNj41zbAaBx48bR5cuXSSwW09q1a+nXX38tyaIzClB0ilq1BbLKJBaLycfHh8aOHUuenp65licnJ8sC0Z49e9LQoUPJ3d0913qsRZb5kSpb6wBQWFgYVahQgbp27arUtBnFlXSLLBFRt27d6NmzZ3Tr1i1atGgRTZ06le7evUu1a9cukfxVaffu3dSjRw86fPgwHT16lNatW0daWlo0fPhwGjlyJJmamqos7yVLltCECRNo4sSJNGXKFNmdOHl1nJCQQEOHDqWDBw/S5s2bqUePHiorF6M648ePp7Vr19Ljx4+pQoUKdP36derYsSP5+/vne2H46dMncnR0pObNm9M///xTcgVmFKbw8VhdfRqUadOmTTAxMYGHhwc8PDxkD9JkT1EbHh4OJycnuLi4YNy4cQqnW977yDIFK8n+k4x6qKOOjxw5AiLC4cOHoa+vj5EjR5ZY3qomFAphbm4uG4Q+NDS0xMalFolEsimHGzZsKBsz9cc63r9/P8zNzWFmZobIyMgSKRujGl++fIGJiQkCAwMhFosxbtw48Hg8vHr1qsBtU1NTy8yYsUzeykWLrKokJyeToaFhvi2yDg4OrEW2HJNIJHTnzh1Wz+WYOupYLBZT1apVKTExkTQ1Nen58+dkYmJSInmXhAMHDtDr169p4MCBpKenV+L537p1i/r06UMvXrygOXPm0IgRI+j+/ftUvXp1Gjt2LG3YsIG8vb0pPDycKlasWOLlY5Rr2bJl9Pvvv9O1a9fI29ubPD09acuWLeouFlNCuOouAMMwzM+Gz+dTv379KD09nWbOnFmugliibw/Vjho1Si1BLBHRr7/+StHR0TRs2DAaP348tWrVik6dOkW//vor7d69myIiIujAgQMsiC0ngoKCqHr16tS+fXt6//49jRs3Tt1FYkoQC2QZhmHUYNSoUTRt2jQaOnSouotSLmlpadHixYspKiqK3r59S3/88QdZWlrS3bt3aeDAgcUazYQpXQQCAS1YsIASEhLIzc2N7O3t1V0kpgSxsSYYhmHUoGLFijRz5kx1F6Pc8/DwoNu3b9PGjRspODhY9gAYU7506tSJ5s+fTzVr1lR3UZgSxvrI5qOgPrIMwzAMwzCM+rBANh8AKCUlhfT19dltKIZhGIZhmFKGBbIMwzAMwzBMmcQe9mIYhmEYhmHKJBbIMgzDMAzDMGUSC2QZhmEYhmGYMokFsgzDMAzDMEyZxAJZhmEYhmEYpkxigSzDMAzDMAxTJrFAlmEYhmEYhimTWCDLMAzDMAzDlEkskGUYhmEYhmHKJBbIMgzDMAzDMGUSC2QZhmEYhmGYMokFsgzDMAzDMEyZxAJZhmEYhmEYpkxigSzDMAzDMAxTJrFAlmEYhmEYhimTWCDLMAzDMAzDlEnlJpC9fv06ubi4kLu7O/Xo0YNEIpFs2blz58ja2pqaN29Onp6eCqcJgJKTkwmAKorMMAzDMAzDFEO5CWStra3p7NmzdOHCBbKxsaEDBw7kWN69e3c6d+4cnTlzRuE0U1JSyNDQkFJSUuQuv3fvHkml0mKVuzS6efMm3b17V93FKBWkUmm5rWfmG1bH5R+r458Dq+efU7kJZC0sLEhbW5uIiAQCAXG5OT/a3r17qVmzZrRs2TKl5SkSicpda61UKqVu3bqRj48PZWZmqrs4agegXNYz8z+sjss/Vsc/B1bPPye+ugugbG/evKGTJ0/SlClTZO85OjrSkydPiIjI19eX3NzcqFGjRrm2zcrKoqysLNn/qampBeYnkUiUUOrS49y5c/Tq1SsiIlq5ciWNGTNGzSVSr+z6LW/1zPwPq+Pyj9Xxz4HVc/nC4/EUWq9cBbLJycnUp08f+ueff0hDQ0P2vp6enuxvb29vunv3rtxA9u+//6aVK1fK/ldkZ7h//34xS126LF26lKysrMjJyYlCQ0PJ0dGR9PX11V0stStv9czkxuq4/GN1XH59/vyZhg0bRoMHD1Z3URglkRenycNBOWmDF4vF5OPjQ2PHjs31QFdycjIZGBgQEVHPnj1p6NCh5O7uniuNH1tkk5OTydLSkpKSkmTbfy86Oprs7e0VumqQSqV09+5dcnBwIA6HU9iPVyJSUlLI0tKSJkyYQAMGDKBatWrRiBEjaM6cOeoumtpIJBK6f/++wvXMlD2sjss/Vsflm1Qqpfbt29OpU6eoXr16FB0dzeq5HPjpWmS3b99O165do9mzZ9Ps2bMpKCiIzp49S3///Tft2rWL1q5dS3w+n1xdXeUGsUTf+tYKBALZ/4p0GOfxeAp92atWraJRo0bR/v37ydfXV/EPVoL27dtHGRkZFBAQQFZWVjRmzBj666+/aMSIEWRpaanu4qmVovVcXl26dIkmT55MO3bsIAsLC3UXRyV+9jouioSEBNLW1i4zd21YHZdPixYtotOnT1NQUBCtXr2aHj16RA0aNFB3sZiSAiZPSUlJICIkJSXJXX7z5k2IxeIC03n+/Dm0tbXB5XLRtm1bZRdTadzc3NC6dWvZ/4mJiTA1NUVgYKAaS6VeYrFY4Xoua6RSqULrPX/+HKampiAi/P777youVckrz3WsSnv27IGenl6OY0Zpxeq4/Lp06RJ4PB4mTZqE9PR0GBsbY+TIkeouFlNMqampSElJUWjdcjNqQWkllUpp4MCBVLFiRVq6dCmdOHGCXr9+re5i5fL8+XO6dOkS9e/fX/aeoaEhTZkyhdatW0ePHj1SX+EYpQsLCyMLCws6d+5cvuslJiaSt7c3GRsbU3BwMK1Zs4Y+ffpUMoVkSiWxWEwTJkwgPz8/qlOnDp06dYrOnj2r7mIxP6EvX75Qjx49yNnZmWbOnEkCgYDat29PW7duJaFQqO7iMUWUkZFBjRs3pj/++EOh9Vkgq2KrV6+m8+fP07p162jgwIFkYGBA4eHh6i5WLv/88w8ZGhpSp06dcrwfFBREVapUoUmTJqmpZIyy3bx5k0aPHk1ERG3atKFt27bJXU8sFlO3bt3o/fv3dOTIEZo5cyYRkVKHsGPKlo8fP1KbNm1o8eLF9Oeff9K///5LjRs3pkmTJrEhj5gSBYAGDBhAqamptG3bNuLzv/WU9PX1pc+fP+caS14ZgoKCqGrVqtSnTx9at24dPX/+nP3uVWDChAn08uVLCgoKUmwDVTYNK+LOnTvqLkKeitu14MWLF9DV1UVQUJDsveHDh6NixYrIyspSenmLSiwWw8rKCkOGDJG7fMuWLSAiXL58uYRLpn7l7ZZkUlISfvnlFzg6OiIlJQV9+/YFEWHevHk5uhpIpVIEBQWBz+fjzJkzsvfHjBkDQ0NDJCYmqqP4KlHe6lhVrl+/Dmtra5ibm+Ps2bOy90+fPg0iwoEDB9RYuvyxOi5/li1blut3l13PLi4u8PLyUmp+iYmJ0NTURIsWLdCoUSNwuVwQESpXroyePXsiKipKqfn9rI4fPw4iwvLlyxXeRu2BrIeHh7qLkKfiBLISiQTNmzdH1apVkZycLHv//v37ICLs3r1bJWUuipMnT4KIcPXqVbnLJRIJGjRoADc3N4X7VZYX5ekEKJVK4e/vD319fTx//lz23rRp00BECAoKgkgkAgAsX74cRITw8PAcacTGxkIgEGDevHklXn5VKU91rCqbNm2CQCBA48aNERMTk2t5y5YtUa9evQK/w3fv3uHBgweqKmaeWB0X3bt37xAWFobLly8jPT1d3cUBAERHR0MgEGDUqFE53s+u54iICHA4HLx+/Vppea5fvx4cDkf2+09MTMThw4cxfvx41K1bF/r6+oiLi1Nafj+jT58+wcLCAl5eXpBIJApvVyKB7MiRI+W+RowYAQcHh5IoQpEUJ5ANCwsDEeH06dO5ljVt2hStWrVSalmLo0ePHqhdu3a+QWr2VVJpbnVRhfJ0AgwPDwcRYceOHbmWRUREgMfjoUOHDti9eze4XC7Gjh0rN53BgwfD3NwcaWlpqi5yiShPdawKHz58gJaWFnr16oXMzEy561y9ehVEhM2bN+eZztu3b2FlZQUigouLCzZt2oSMjAxVFTsHVsdFI5VK0bJlSxARiAh8Ph8NGzbEkCFDEBERgf/++6/Ey5SVlYUaNWrg119/zfV7zK7nxMRE6OnpYfr06UrLt1WrVmjevLncZZ8/f4apqSn69euntPx+NlKpFJ07d4aJiQnevXtXqG1LJJB1cnJCVFQUrl27luP177//wsXFpSSKUCRFDWRfvXoFPT09DB48WO52GzduBBHh2bNnSi1vUXz9+hVaWlpYsGBBvutJpVK0atUKurq6WLx4sazlrrwrLyfA+/fvQ0tLK98RKI4dOwY9PT0QEXx8fPL8zC9evACPx8OyZctUVdwSVV7qWFUmT54MPT09fPnyJd/1fH19Ub16dQiFwlzLPn78iNq1a8PGxgabNm1Cq1atQEQwNTXFuHHjZHcIVIXVcdGsX78eRITDhw8jOjoaq1evRkBAAOrVqwcOhwMigp2dHUJDQ/HixYsSKdPBgwdBRLh161auZd/Xc2BgIKytrZVS53FxceByubnuUH3v77//BhHhypUrxc7vZ7Rhw4Yi360ukUB22LBhuH79utxl/fv3V1o+ISEhcHNzQ+/evXP0QRWLxQgICICbm1uuWxH5yS+QffHiBezt7dGqVSv0798fkydPRlhYGA4cOICWLVvC2to6zwA4e4iQkJCQQn/GooiLi8uzLGvWrAGXy1XolkhiYiJGjBgBLpeL+vXr/xR9ZsvDCTAtLQ1169ZFvXr1CmxFvXXrFkaOHFngsCe9e/eGlZWV3KClNJJKpVi8eDGWLl2a685DXnX86tWrPLvb/CySkpJgZGSUZ+v89+7fvw8Oh4OwsLAc76ekpKBx48YwNzfH06dPZe8/efIEv//+O4yNjUFEGDZsmNLLn6087Mcl7f379zA2NkafPn3kLk9OTsbBgwfRs2dP6OrqgojQuHFjLFmyRKW32P38/NCgQQO5y76v53///RdEhOPHjxc7zyVLlkAgEOR7MScWi/Hrr7+iUaNGJfo7K+gCU5mSk5Nx7NgxTJw4Ec2aNYOjoyNcXFzg4eGBVq1a4bfffkPnzp1x8eLFQqX78uVL6OvrF7lFW+19ZJXlzp076NWrFwAgNDQU27Ztky3bv38/Jk+eDAAYNGiQwldM+QWyX79+RZ8+fdC5c2c4OzvD2toafD4fRAQul4sTJ07km/aoUaNgbm6u8kAgISEBpqamMDAwwIQJE3IdYJo0aYLffvutUGnevHkTjo6OICIMGjQInz59UmaRVUYoFOLhw4eF6ntTHk6AAwcOhLa2Nh4+fKi0NB8+fAgiQkREhNLSVBWxWIzBgwfLbo/6+fnlCNTl1fGuXbugr68va51+8uSJOoqudgsXLoSGhgZiY2MVWr93796wsLCQXTBlZmaidevW0NfXR3R0tNxt0tPTMXXqVBBRjkBXmVS9HwuFQly8eLFUPcRbXN27d4eZmRk+fvxY4LqpqanYvn07fHx8oKGhAYFAgE2bNim9TF++fIFAIMDixYvlLv++nqVSKerVqwc/P79i5+vo6IiOHTsWuN7ly5dBRFi7dm2R8pFKpYXqsjV37lxwOBwEBwfn2VhVHGKxGCdPnkRISAgaN24MHo8HIkLFihXRtWtXDB48GAEBAejVqxe6du2Kjh07ok6dOjA1NcXbt28VzsPV1RU2NjZF/gwqCWRPnTqlimTzFRYWho0bNwL4Fmh9f3UfEhKC8+fPA/g2iPeiRYvkpiEUCpGSkiJ7vXv3rsCuBUKhEGKxGGKxGFlZWYiNjcXbt29l7+X1yn7oa9u2bQWuW5xXr169YGJigtGjR0NfXx8CgQCBgYF49OiRrAw7d+4sdLpCoRArVqyAoaEhzMzMMGPGDGzZsgXnzp3DixcvkJGRodLPpejr1atXWL16NXx9fWWBiYeHB168eKHw5/yxnkv76+vXrzh58iRCQ0PRvn172UNbys6nY8eOsLW1RWZmpto/c16v1NRUdOrUSXZbMHsQf3t7ezx9+jRXHaelpSE4OBhEhG7dumHjxo2oWrUq+Hw+Ro4ciYSEBLV/ppL87ipVqoSBAwcqvM2TJ0/A5/Mxb948CIVCdO3aFQKBAKdOnSowL1NTU4waNUoln0UV+7FIJMKlS5cQHBwsmzBk/PjxKim/SCTCzJkzsWXLlhKp+wMHDoCIsGnTpkJv+/HjR/Tv3x9EhD/++ANZWVkFbpOUlKRQ2qtWrQKPx0NsbKxC9fzXX39BQ0MD8fHxRf4u/vvvP9mzBYqs37t3b9kFQGHzGjFiBPT19XHhwoUC192xYweICN7e3tDV1YWlpSX27t2rlPp///495s2bBxsbGxARLCws4O/vj7CwMDx8+BAikSjPbT98+ABra2u4uLgoFAdMnz4dXC4X586dy7VMURxA+YOg1atXj0JCQqhv3755rgOAOByO0vKcO3cu1a1blzp27EjPnz+nadOmycbHHDx4MAUHB5ODgwOdPn2azp49S3Pnzs2VxooVK2jlypWy/yUSCb148YKSkpLIwMAg1/rR0dHFKnNgYCBxuVz6+++/c7wPgE6dOkXh4eGUmppKPB6P+Hy+bHpFLS0tGjp0KDVt2jTf9P/9918aPnw4TZs2jXx8fCglJYX27NlDO3bsoC9fvlDlypUpNTWVjh07lmNq3sL49OkTLV++nM6fP09paWmy9zkcDpmampKdnR2NGzeuRKc1jY2NpX379tGlS5fo5cuXxOVyyd7enpo2bUpWVla0fPlySk1NpfHjx1P79u2V9juMjY2lVatWUa1atahv377E5ZbMMM1ZWVl06dIlunTpEj148IBevXpFAEhXV5fq1q1L7u7u5O/vr9T9jYjo0aNH1KdPHwoNDaW2bdsS0bcJQN6+fUt3796l169fk6+vL9nY2Cg1X0WlpqbS2LFj6cGDBzR37lzy8PAgIqIXL17QuHHjKCkpiebOnUvOzs5E9K3+Jk6cSC9evKCxY8dSly5diMPhUGZmJm3fvp02bNhAGhoaFBgYSH5+frJxK8uryMhImjt3Lu3Zs4eqVq2q8Hbz58+nkydPUvPmzenw4cO0YMECatGiRYHbLV++nCIjI+no0aOkra1dnKKrVGxsLB09epSOHTtGMTExZG5uTm3btqXMzEyKjIyknTt3Fur7UsS+ffto7ty5ZGBgQAcPHiQ9PT2lpv+9tLQ06tatG1WvXp2WL19epOMGANq4cSOtXLmSWrduTdOnTyctLa1c67169YrCwsLo/PnztGrVKnJycso33QEDBpC+vr7CY1knJiZSu3btaNiwYdS7d+9Cfw4ior///pu2bdtGJ06ckPsZfvTp0yfq0qULdejQgcaPH69wPvv376fQ0FCytLSkxMREWrlyJdnb28td98GDBzRkyBBq3rw5hYaG0ocPH2j+/Pl06dIlatGiBYWEhJC5ubnCeX+f7u7du+nUqVNEROTl5UV+fn5kZ2dXqN/BvXv3KDAwkHr27EmjRo3Kc72tW7fSkiVLaPDgwTR48OBcyxs1aqRYhgqHvIUQFRUFBwcHhIaG5lomFouxd+9etGnTRql5rlq1Ks8W2fHjx5dIi2xhX5s3bwYR4eHDh7L37t69i+bNm4OI0KFDB0ybNg2TJk3C+PHjMWbMGIwcORJNmzaFnp4ebt26lWfaKSkp+OWXX+Dh4ZHr6ik1NRVhYWGoU6cOpk6dqpQrOLFYjC9fvuDevXs4cuQI1q5di6lTp8La2hr6+vpYu3ZtvldxMTExCAoKgouLC1JTU4uU/507d9CzZ0/weDzZE6Tbt2/PdWX8+fNn9OnTB0SETp064f3793mmqUhLTnp6OkJDQ6GlpYWKFSuCw+HAy8sr33SL+xKJRLh69SqCg4NhYmICIkK9evUQGBiI8PBw3L17t0Rakb28vFCnTh3MmjUL7dq1k/V35HA4MDY2hra2NlasWJFv3aviFRsbiwYNGsDIyEjulf7Hjx/h5eUFLpeL+fPnY/78+TAwMMAvv/yCGzdu5JnmwIEDweFw4OLiolBLU1l9CYVC2NraokuXLoXe9u3bt9DS0gIR4e+//1Z4u+fPn4PD4WDNmjUq+TzFPV6LRCIsWbIEPB4Penp66NevH06cOCFLMyUlBdWqVYOXl5dSf+9Xr16FQCBAt27dIBAIMHv2bJXW/fDhw6Gjo4Pnz58XO61du3ZBW1sbjRs3xrt373LUdd++fcHlclG1alXUqlULjRs3zvd7e/ToUYF3MeXVc7du3VC3bt0i1YlIJEKNGjXQt2/fQm23YMEC8Hg83L59W6H1L126JLtbmpiYCFdXVxgYGODKlSu51n3x4gUqVqyY61wpEomwfft2VKxYEQYGBlixYgXS09MV+oyRkZGy7oI2NjaYN29esc9fixYtAhFh//79cpfPnTsXRIQJEybkWTeKUlkf2QcPHsDNzQ3Dhg1DZmYmhEIhtm7dihYtWsDR0VHpTzzfvn1b1il9zpw5OfrIRkZGYurUqQCAwMBApfSRBQqeEKEgmZmZMDU1xe+//46kpCT8/vvv4PP5sLW1xbFjx/LcLjU1FQ0bNkSVKlXw/v17uev88ccfEAgEePz4cZHLpwyJiYkICAgAEeG3337LNazG169fMWnSJOjo6MDIyAgcDgfr168vVB7//vsvfH19QUSwtrbG8uXLFepntHv3bpiYmKBixYo4fPiw3HXE4vz71l28eBF2dnbg8XgYN24cUlJScOLECZiZmcHS0hKXLl0q1GcpSEJCAubPn486derIBuMOCQlRav/Xwrh48SI4HA4MDAzQpk0bzJw5E6dOnUJSUlKO2/Tt2rVDfHx8odMXi7/detq/f7/C2zx79gzVq1dH5cqVce/evXzTnjBhQo6+s4r00coed7k0jQWtbDt37gQR4ebNm0XaftOmTVi3bl2ht/P29kaDBg2UPl51QftxQTIzMzFw4EAQEcaOHZvn8WX//v2yk7cyfPz4EdbW1nB2doZQKMSwYcNgYmKSY2xyZbp69So4HA7++usvpaV548YNVKpUCVWrVsXZs2cxYsQIaGhooGLFilixYgUyMzNx6tQpEBEOHTqUZzpTp06FgYFBvmPZyqvn7P21KA8n37hxA0SEkydPFmo7oVCIWrVqwcPDo8Df8rt372BhYQFXV1fZMzPJyclwdXWFoaEhbty4IVs3KSkJ9vb2sLGxwYcPH+Sm9+XLFwQGBsq6BMyaNUtunCCVSnH06FFZAOvh4YFDhw4VK6b5MX1fX18YGxvnGs931qxZICJMnz5dKfu6Sh/2iouLQ4cOHeDt7Q03Nzc4Oztj9erVBT4RXVTjxo2Dm5sbevbsCaFQKBv+SiQSoV+/fnBzc8OIESMUTk/VgSwAjB07FoaGhrCwsIC2tjbmzJmT51iN34uJiYGFhQWcnZ1zjcV479498Pl8zJw5s1hlU6ZDhw6hUqVKMDY2xrZt25CWloYFCxbA2NgYOjo6mDRpEr5+/YoOHTqgfv36Cv24P378CE9PTxARatWqhfXr1xf64bm4uDi0a9cORIQRI0bkOS7hj/X8+fNnDBo0CESEJk2a5JqhLjY2Fq6uruDxePjzzz9zfJ60tDQcOnQIQ4YMQZUqVeDk5IRz587lW06xWIywsDAYGRlBS0sLPXr0wPHjx5V20CmODx8+5FuOI0eOoEKFCjAzMyvUCV4qlWLEiBGyQDMgICDfE7hYLMby5cuhr6+PWrVqKTwY+r59+zBjxoxCDSnXunVr1KtXr1APDpYm+X1WqVSKhg0bonXr1iVYom+yx6tW9gVgcQLZDx8+wNXVFQKBAP/880++60qlUrRt2xY2NjbFnjxALBajVatWMDc3lw3CHxMTA4FAgLlz5xYrbXmEQiHq1asHR0dHpR9X3r59iwYNGoCIYGRkhHnz5iE1NVW2XCqVwt3dHQ0bNpR77JdIJKhatSoGDRqUbz7y6lkikaBWrVqoU6dOoR9MHj16NCpWrFik4Sazf8vyxu3OlpmZCWdnZ1haWua60E9KSoKLiwuMjIwQHR0NkUiEdu3awcDAQKGGiwcPHmDIkCHQ1taGQCBA3759cfPmTUilUpw8eRLOzs4gIri6uuaYqU+Zvnz5AhsbGzRu3BhCoRBSqRRTpkwBEcm9Y19UKgtkU1JSsGbNGjg7O6N+/fpo0KCB2lsHC6skAtmnT59CS0sLXbp0wZs3bwq17fXr12VBTfbOL5FI4OzsjNq1aysUEJekT58+wd/fH0QEAwMD8Pl8BAcH5xhJIXu6S0V2rOHDh8PQ0BC7d+8uVj1IpVKsWLECGhoacHJywsuXL2XLfjwwisVihIeHw9zcHAYGBli1alWeeWdlZSEkJAREBF9fX4SFhaF9+/ay266//PILRo4cicaNG4OI0LlzZ7njaUZHR8PJyQlEhIEDByr0FHFp8+HDB3h7e4OIEBgYmOMklpf58+eDiLB69WqsW7cOenp6qFatmtyhXW7cuIFGjRqBw+Fg6NChhRqSpihBzqVLl0pNq+yDBw8wZ84chVu8b9y4ARMTE3h5eck9IWafgL+fmrikSCQS1KhRA/7+/kpNt6iB7O3bt2FtbY2KFSsqPBTbkydPoKGhUeyGhEmTJoHL5eaqh6CgIJiamiq1Qejp06fo0KEDeDyeyqaNT05Oxvr16/PcN8+fPw8iwt69e3MtO3fuHIiowGGd8qrnJ0+ewNzcHE2aNFH4exOLxahUqVKhhuz8UceOHcHn89GtWzdcvHgx1zTgAwYMgKamZp7DkyYmJqJJkyYwNjZGt27dwOPxChwR6UefP3/GokWLULVqVdldy+wGmJMnT6p8ts7r169DQ0MDo0ePlt0BW7hwoVLzUEkgu2TJEjg5OcHT0xM7d+5EWloaJkyYABcXF9y9e1cVWapESQSyAIo1u82uXbtARJg1axaAb32FiQgXLlwodrlUZffu3fj999/lBm3ZQ6b4+Pjkm8br16+hoaGBOXPmKK1cN27cgI2NDYyMjGQth98fGK9evYpGjRqBiNCnTx+Fx0o8ePAgjI2NwePx0Lx5c/z55594/PhxjouPLVu2wMrKCgKBAOPGjUNiYiISExMxfPhwcLlc2NvbK72VqqRJpVKsXbsWOjo6qFWrVr4nzOxJQ7K7BAHfxm52dXUFl8vFH3/8AaFQKPuOOBwOGjRoUKRxX4sa5KizVTYjIwObN2+Gm5ubrMW6evXqBU4scPPmTRgZGeHXX3+Fra0teDweRowYgc+fP8vWad68OZycnNQ2HfWSJUtkT5rn5cSJExg6dKjCt9iFQiF2796t8NBGUqkUu3btgo6ODho1aiR3Wt78TJgwAVpaWnj16pXc5VlZWdi8eTN2794t9xZxdhcFeRPVvHnzBhoaGpg/f36hyiRPQkIChg8fDj6fD2tra+zZs6fYaRZH69atYWdnl2tfHDBgAKpVq1bgbzK/ffnmzZvQ19eHl5eXQnfusrs75BVkKiI1NRXLly9HzZo1QURwcHBAREQE0tPTsXLlShBRga38iYmJsoaMH8doLgyxWIzIyEgMGDAAR44cKdH9e9myZbLj1NKlS5WevkoC2bZt2yIyMjLXj2nJkiVwcHCQO21raVRSgWxxzZ49W/YDMTAwKPD2S2mXPU92fjOfBQQEoEKFCkrvpvLlyxdZf9vff/8dGRkZOH78OPr27QsiQsOGDYsUUCYnJ+Pr16/5rpOWloZZs2ZBR0cHZmZmqFSpkmwmtfI0PuWjR4/QoEEDaGpqYtWqVbkOqMeOHQOfz8fAgQPlTl4wd+5c8Pl81K9fH5UqVYKenh6WLFlS5NnmihrIZrfKluTJP3sSgewH/Fq2bIldu3bhyZMnqFGjBipWrJjnBUJ2ENukSRMkJiYiMzMTCxcuhL6+PkxMTLBq1SrZZ9q3b1+JfaYfff36FTo6OrKL8x+dPXsWmpqaICI4OTkVeIciOTlZNgydlpYWWrRogRkzZiAqKkrWiJCRkYGLFy9iwYIF8PHxgZmZGYgI/v7+RZqKOSUlBZUrV0anTp1yvC+VSnHw4EFZYJP9qlOnDoYOHYrt27fj8uXLMDAwQKdOnfIMNoYOHQozM7MiH//S09Mxd+5cGBgYwMDAAPPnzy92VwhlyJ7E4PtnXNLS0qCvr6/QdLMF7ctnz56FQCBA9+7dC9zfAwICYGtrq5SATyKR4Pjx4+jQoQM4HA5MTEzA5/MVbu1NSkoqM3GTPFKpFBMnTsSGDRtUkr5KAtn8Kn7nzp2oV69evnNylxZlJZCVSqXo1asXiAgVKlQo0Zk+VCE9PR2mpqYYOXKk3OWPHj0Cl8tV2RSpUqkUf/31F/h8Pho0aABdXV2YmprKnsBWtdjYWAwePBj9+/cvdEtQWZGRkYFhw4bJulRk/2Zv3LgBXV1dtG/fPt/A9NatW2jYsCH8/PyK/R0Vp/9k69atYW9vn2+rbEREhKx/uIGBAfT09KCjowNNTU0YGxujV69eOHjwYJ5dgT58+IAVK1agSZMmsmldx44dm2uShg8fPuDXX3+FoaFhrjsy0dHRMDY2lgWx34uPj8eAAQPA4XCgoaGBWrVqqb3vb2BgICpXrpzrAu7q1avQ1dVF69atcfXqVVSoUAG1a9fOc/D1169fw97eXhYILVq0CD4+PrLRNQQCAerVqweBQAAigq6uLlq2bImpU6fi1KlTxQpitm3bBiKS3Qq+c+eOrE+/p6cn7ty5g9jYWGzduhWDBw9GrVq1ZIFtzZo1833wMLtVtqCpxX8klUqxZcsW2QQ+I0aMKHVdlTp06IAaNWrI9v/s71GRaYwV2Zf37t0LLpeL4ODgPOs3IyMDBgYGCgXPhfX8+XP8/vvvGDhwYLlqoFAntczsde7cOTRs2FAdWRdKWQlkgW87Xr9+/XD06FF1F0Upsud3//GkCwBdu3aFtbW1yvsAX716Ffb29ujatSsSEhJUmtfPat++fTA2NkaVKlWwfft2VKhQAY0bN1aoD62yFCeQLahVNiIiQtayt2DBAvz555/466+/sHTpUqxYsQJTp06FnZ2drN94nz59cOjQIXz+/BlbtmxBu3btwOPxwOfz4e3tjR07duTbFSkpKQktWrSAlpYWDh48COBb0G9sbIzGjRvL3Z+y3bx5E97e3vk+OV5S7ty5k6sP8u3bt2FkZAQ3NzfZ7+Pp06eoWrUqrK2t8ejRoxxpXLlyBRUqVEC1atVw9+7dHHUskUhw584dLFu2DIGBgVi+fLnsgRplyX6AqVatWrJh22rVqoXDhw/nGUDFx8djz549Cj2oOHjwYJibmyu8r1y/fh0uLi6yi0dVzaJWXLdu3QIRyVrv2rRpA1dXV4W2VXRfDg8Plz01L8+ePXtARD/tjH5ljdqmqH3w4IG6slZYWQpky5t3796Bz+fnGgYm+yBXlKF9iqK4w/YwBXvz5g1cXV1BRKhRo0aJXzQUt47zapVdv349OBwOgoKCCmzZe/DgAaZPny4bVi375ebmhtWrVxfqaeuMjAx06tQJPB4PM2bMgImJCZycnArs2lLauLm5oXnz5gC+3YUxNzdHo0aNcgXjsbGxsLOzg6mpqaw/45YtW6CpqQk3NzfZbGzq2I/v3r0rG9d65cqVSm2Be/XqFfh8fp7jomeLj4+XDYFob2+vsifUlalLly6wsbHB69evweVyFZ7ytTD1PG/ePBARGjVqBCcnJzRq1AgODg6oX78+zMzM4OjoWNyPwZQQtQWyynTt2jU4OzujWbNm8Pf3z3WwiIqKgpWVFTw8PNCyZUuF02WBrHr16tUL1apVy/Ed//bbb6hZs6ZSW07ywwLZkiESiRAREaHw/NzKVNw6ltcq+88//4DD4WDIkCGFvk3/4MEDRERE5PmgkCJEIpFs3NOyGMQCwPbt22Vji1paWsLOzi7PgP7z589wdnaGnp4eBgwYACJCv379ZHdt1Lkf37lzR2Xff2BgICpUqCC3H29ycnKOPtBhYWEldtwsrgcPHoDD4cDBwQGampoKf3+FqWepVIpVq1Zh4MCBCAwMxJAhQxAcHIzhw4dj5MiRZf7h2p9JuQhk4+LiZB3VJ06cmGtInKioKIwdO7bQ6bJAVr2yB6OOjIwE8G3wfSLCzp07S6wMLJAt/5RRx9+3ym7atAkcDgeBgYFq7Wua/WBRft0JSjOhUIhKlSqBiGBra1vgKCGpqalo27YtiAjz58/P0QpeXvfj7FbZVq1awcfHB02aNIGNjQ10dHRARHJHpSgrevbsCSJCt27dFN6mvNYzk79yMVm4hYWF7G+BQCB3jvu9e/fStWvXyM/PL9+5f5nSw9HRkVxdXWnp0qXk6+tLkyZNogYNGpCfn5+6i8YwOUyfPp3c3Nxo4MCBtGnTJhowYACtWbNG7rGopHA4HPL29lZb/sUlEAgoJCSE1qxZQydPnsxxnJdHV1eXDh06RG/evKFffvmlhEqpXjY2NjRlyhQ6dOgQCQQCqlu3LrVo0YIqVKhAFStWJEdHR6pZs6a6i1kk06dPpwMHDtDgwYPVXRSmlOMAgLoLoSxv3rwhf39/unDhAmloaMjeT01NJYFAQEREvr6+FBoaSo0aNcq1fVZWFmVlZcn+T05OJktLS0pKSiIDA4Nc60dHR5O9vT3xeDwVfBqGiGjPnj3k7+9Pc+bMocmTJ9OBAweoffv2JZa/RCKh+/fvs3oux5RVx23btqXTp09Tv379KDw8XK1BbHkCgDgcTrHSYPtx2SQSiXKcywvC6rl8UbQOy1Qg+/79e/L398/1/o4dO0hHR4c6dOhA4eHhVKtWrTzTCAsLIy0tLRowYECuZStWrKCVK1fK/pdIJPTixYt8A1lGtcRiMfn6+tKHDx+ofv36tG7dumKf1BhGFWJiYujixYvUvXt3dhJlGIYpJnkNjvKUqUA2L2KxmHx8fGjs2LHk6emZa3lycrIsEO3ZsycNHTqU3N3dc63HWmRLpz///JMmTpxIp0+fpubNm5do3uwKv/xjdVz+sTr+ObB6Ll8UrcNy0Ud2+/btdO3aNZo9ezbNnj2bgoKCqHv37jRkyBD6+++/adeuXbR27Vri8/nk6uoqN4gl+tYnK7sLAhGRVCotMG8ej8d2GBUbM2YMubi45FlvJYHVc/nH6rj8Y3X8c2D1/HMpFy2yqpKcnEyGhoZ5tsgyDMMwDMMw6sMC2XwAoJSUFNLX12f9MhmGYRiGYUoZFsgyDMMwDMMwZRIbH4ZhGIZhGIYpk1ggyzAMwzAMw5RJLJBlGIZhGIZhyiQWyDIMwzAMwzBlEgtkGYZhGIZhmDKJBbIMwzAMwzBMmcQCWYZhGIZhGKZMYoEswzAMwzAMUyaxQJZhGIZhGIYpk1ggyzAMwzAMw5RJLJBlGIZhGIZhyiQWyDIMwzAMwzBlEgtkGYZhGIZhmDKJBbIMwzAMwzBMmcQCWYZhGIZhGKZMYoFsPgBQcnIyAVB3URiGYRiGYZgfsEA2HykpKWRoaEgpKSlyl9+7d4+kUmkJl4opSVKplNVzOcfquPxjdfxzYPX8c2KBbDGIRCLWWlvOAWD1XM6xOi7/WB3/HFg9K1dmZiYFBwfTvXv31F2UfLFAlmEYhmEYhslh7ty5tHr1aurfvz9JJBJ1FydP5SaQvX79Orm4uJC7uzv16NGDRCKRbNm5c+fI2tqamjdvTp6enmosJcMwDMMwTOn28OFDmj9/PnXp0oXu3LlDq1evVneR8lRuAllra2s6e/YsXbhwgWxsbOjAgQM5lnfv3p3OnTtHZ86cUVMJGYZhGIZhSjepVEqBgYFUvXp12rp1KwUGBtLkyZPp/fv36i6aXHx1F0BZLCwsZH8LBALicnPG6Hv37qVr166Rn58fjRo1Sm4aWVlZlJWVJfs/NTW1wHxLc3M7U3zZ9atIPWdmZpK3tze5urrSjBkzVFwyRlkKU8dM2cTq+OfA6lk5Vq9eTVevXqWoqCji8/kUGhpK+/bto7Fjx9KmTZtKrBw8Hk+h9TgoZ72i37x5Q/7+/nThwgXS0NAgom8BqUAgICIiX19fCg0NpUaNGuXadsWKFbRy5UrZ/xKJhF68eEFJSUlkYGCQa/3o6GgVfQqmOADQ48ePqXbt2sThcEos34ULF9Lu3bsJAM2ePZvatWtXYnkzjDJkZmaSpqZmie43DMOUHgkJCdS1a1fy8vKiyZMny94/dOgQzZw5k1avXk1OTk4lUhZ5cZo85SqQTU5Opg4dOlB4eDjVqlVL7jphYWGkpaVFAwYMyLXsxxbZ5ORksrS0zDeQtbe3V/iqgSkZGzZsoMDAQFq/fj317du3WGlJJBK6f/9+gfV88OBB6ty5My1btoyio6Np165ddP78eYV3REZ9FK3j0urdu3fUvXt36t+/Pw0cOLDIQej58+fJz8+PatasSWvXriU7Ozsll1R9SqKOs7KyZA0mjHqU9X1Z1U6fPk0jRoyggIAAGjNmjKyx73tdunSha9eu0YMHD8jIyEj2vlQqpRYtWtCnT5/o9u3bJfJbV7gOUU6IRCK0a9cOp0+fzrUsKSlJ9nePHj1w/vx5hdJMSkoCEeXY/ns3b96EWCwuWoEZlUhKSkKFChXA5/NRvXp1ZGVlFSs9sVhcYD3HxMTAxMQEvr6+kEqlyMjIQOPGjWFlZYX3798XK39G9RSp49JKKpXC19cX2traICJ06tQJnz59KnQ6W7duhYaGBjw8PFCrVi1oaGhg+vTpyMzMVEGpS56q6/jChQvQ09PDypUrVZI+o5iyvC+r2p49eyAQCGBnZwcejwc7OztcunQpxzr79u0DEWH37t1y07h//z54PB7mzp1bEkVWWLkJZDdt2gQTExN4eHjAw8MDO3bswODBgwEA4eHhcHJygouLC8aNG6dwmuUhkE1MTMTQoUOxfft2dRelQPfv38fGjRuLdfIcP348dHR0cPToUXA4HKxdu7ZYZSrowCgWi+Hu7g4rK6scAURsbCwqVaoEV1dXCIXCYpWBUa2yfPLbs2cPiAh79uzBvn37YGJiAktLS5w9e1ah7aVSKebMmQMiQr9+/SAUCpGRkYHJkyeDz+ejbt26uHr1qoo/heqpso7v3r0LQ0NDGBkZQUtLC48ePVJ6Hoxi1LUvP3nyBLdv31ZZ+pGRkXB1dcWpU6eKtH1ERAS4XC78/f0hFApx+/ZtNG7cGESEQYMG4dOnT0hMTISFhQW8vb0hlUrzTGvcuHHQ1tbGq1evivhplK/EAtmMjAy5rVNPnz4tqSIUWkkEslKpFDdu3IBEIilWOvLcvHkT1atXB4/HA4/Hw8GDB5WehzLExMQgICAAHA4HRFTkk+eTJ0+goaGB2bNnAwC6d+8Oa2vrYgXGBR0YZ8yYAS6XK7eV/8qVKxAIBBgyZEiR82dUr6wGsl++fEGlSpXQsWNH2YknNjYWLVq0AIfDwYQJE/K9iBKJRAgMDAQRYcaMGblOXnfv3oWjoyM4HA5GjhyJlJQUlX4eVVJVHb98+RKVKlVCw4YNER8fj5o1a8LJyQkikUip+SiLVCrFvHnz4OPjo/CdybJEHfvytWvXYGhoCH19fZUEdxcuXICmpiYqVKgAIkKvXr3w4cMHhbdfuHAhiAhBQUE5vhexWIywsDAYGhrCzMwMLVu2hJ6eHt6+fZtveikpKbC0tISPj0+RP5OylUgge+zYMTRr1gw+Pj7o0KED7ty5I1vWsWPHkihCkag6kE1LS0OPHj1ARAgLCytyOj+SSqVYuXIlBAIBHB0d8ezZM3Tu3BlaWlq4fPmy0vIprq9fv2LixInQ0tKCubk5VqxYgVu3bsHJyQkcDgdjxoxBWlqawul16NABVatWRXp6OgDg0aNH4HK5WLFiRZHLmN+B8fz58+ByuZgxY0ae20dERICIsHr16iKXgVGte/fuYc+ePWUukB00aBAMDAwQGxub432xWIz58+eDz+ejUaNGWLRoEXbt2oV///0X8fHxkEgkSE5ORtu2bcHn8/HPP//kmYdIJMLixYuhra2NKlWq4NixY6r+WMWSmZmJW7du5QrKVRHgvH//Hra2trC1tZU10vz777/gcrmYNWuW0vJRFqlUivHjx4OIUK1aNRARmjVrhhMnTuTbAleWlHQg+++//8LAwAAuLi6oWrUqmjVrptS8Hzx4ACMjI7Ro0QIZGRnYsGEDTE1NYWRkhL///jvfBjCpVIqJEyeCiDBlypQ86zg+Pl4Whyh6rsy+EzRhwgR8+fKlSJ9NmUokkPXx8cHHjx8BfLt93L59e1nroK+vr9LyCQkJgZubG3r37p2jb6RYLEZAQADc3NwwatQohdNTZSD75s0bNGzYEDo6OnB0dISVlRUyMjKKlNb3EhMT4efnByLCyJEjZa2RGRkZ8PDwgLGxMR48eFDsfIpDKBRiyZIlMDExgY6ODqZMmZLjOxaJRFi0aBG0tLRQvXp1hW6THjt2TG7fnr59+6JSpUqFCoi/l9eB8dOnT7CysoK7u3uBv4Fhw4aBz+fjwoULRSoDoxo3b96Er68viAimpqYlfkC+ceMG2rRpg+nTp+Pq1auFOpacPXu2wAukGzduwNnZGbq6uiAi2UtTUxPGxsYwMDBQ+Fblixcv0KpVKxARevfuLTuelyYJCQlwdXUFEaFx48Y4cOCA7ESf136cmZmJhw8fFvqOWFJSEho2bIhKlSrh5cuXOZZld8uIjo4u3gdSIolEgmHDhoGIsGzZMkilUhw8eBBOTk4gIjg6OmL//v0quTNYkuTVs1QqxbVr1/I8jxfV1atXYWBgAFdXVyQnJ+P8+fPgcDiYP3++UtJ/+/YtrKysUL9+fSQmJsre//jxI/r37w8iQtOmTXHt2jW8ePECDx48wPXr13H+/HkcP34cAwcOBBHhr7/+Uii/mJgYhS9opFIppkyZAm1tbRgYGGDKlCn4/PlzkT6nMpRIIPvbb7/l+P/r16/o1asXVqxYobQW2Tt37qBXr14AgNDQUGzbtk22bP/+/Zg8eTKAb60YV65cUShNVQWy58+fh7m5OWxsbHDnzh08fvy42C2HABAdHY3q1avD0NAQe/fuzbU8MTERDRo0gKWlJd68eVPo9G/evFns21FCoRDt27cHl8vF4MGDERcXl+e6T58+hbu7O4gIgwcPzjPQyMrKQu3ateHh4ZFrR3z+/Dn4fD7+/PPPIpU3rwNjx44dYWJigpiYmALTyMrKgoeHBypVqlTsh7+kUmmxH2D72V2+fBnt2rUDEaFmzZpYunQptLS0MGbMmBIrQ1xcHCpXrowqVarAyMgIRARjY2N07doVERERuVpZv5eeng5bW1u4ubkpFHhIpVJ8/vwZt2/fxv79+7F8+XJMmjSp0Be0UqkUGzZsgLGxMczMzLB169ZS05L3+PFjVK9eHebm5lizZg2aNWsGIoK9vT22b98OoVAo24+/fv2KrVu3olu3btDX1wcRwdbWFosWLVLoQbmMjAy0aNEChoaGuHv3bq7lQqEQDg4OsLOzU0rjxMePH3H27FksX74cy5cvx+HDh/Hw4UOFL86zG3I4HA7Cw8NzLJNKpTh58qTsOGtqaorKlSvLXpaWlrC0tISrqytevHhR7M+iat8fr5OSkrB8+XLUrl0bRIQRI0YoLZ8rV65AX18fbm5uSE5Olr0/YcIEaGho4NatW8VK/8uXL7Czs0PVqlXx7t07uetERUWhVq1aOS5Sv3/x+XysX7++WOUoSHx8PMaOHQttbW3o6+tj8uTJRXrYtLhKJJDt3bt3rg7wQqEQY8aMQZ06dZSSR1hYGDZu3AjgW8A1bNgw2bKQkBBZALZnzx4sWrRIoTSVHchKpVKsWrUKfD4fLVq0yNGq0adPH1SuXLnIB77Dhw9DW1sbjRo1yveAExcXh2rVqqF27dqF+sE9ePAA+vr60NDQwIkTJ4pURpFIBD8/PwgEAoXTkEgkCAsLg76+PszNzfHPP//kOnkuWbIEXC43R5eV7wUGBsLMzKxIffzkBbLbtm0DEcm9WMhLXFwcKlasCE9PzyK34h8/fhz169cHh8NB5cqV0bRpU/Tq1QuTJ09GeHg4Ll++XGr75pUGly9fRsuWLUFEsLOzw7Zt2yAWiyEWixEUFAQ+n4///vtP5eXIzMyEi4sLKleujLi4OIhEIly+fBnTpk1DkyZNwOVyweFw4O3tjbNnz+b6vU+cOBECgUBtDxW9f/8e3bp1AxHht99+U+hiTpXOnj0LIyMj1K1bN0fr6Pnz5+Hl5SW7YAkMDISnpyf4fD6ICI0aNcKsWbNw8OBB9OzZEwKBAJqamujduzcuX74s+97FYjFiY2Nx5coV7NixA+3bt4eWlla+d1ju378PgUCg8MPFycnJePjwIY4fP47w8HCMHTsWrVu3RqVKlWSBiUAggEAgyBGsVKxYES4uLggKCsL+/ftzBFXAt4tof39/8Hg8bNmyJd8yXLhwAbNmzcLMmTMxY8YMzJgxA9OnT8f06dNha2sLc3NzhRuB1EUsFmPHjh0YMmQIdHV1wefz0bVrV3Tq1AkVKlRQyvHx8uXL0NPTQ7NmzXKdU7IvYurWrSvr4lZYGRkZcHd3h4mJSYH7eGZmJk6ePIkzZ87gypUruH37Nh4/fow3b94ovQU6Px8+fJA9aK2np4eQkBDEx8eXWP4lEsjGx8cjISFB7rKbN28qJY85c+YgMjISAPDs2TP06NFDtiwwMFD2ROGpU6fwxx9/yE1DKBQiJSVF9nr37l2BgaxQKJSdDPN7ZWRkYNCgQbJb/hkZGTmWP3r0CDweD0uWLFEove9fmzZtAo/Hg6+vL1JTUwtc/9GjRzA3N0fjxo2RlJRU4Prx8fGoVq0a7O3t0bZtW+jq6uLy5cuFKmNWVhZ69+4NHo+HyMjIQn/Gt2/fwt/fH0QEV1dX3Lp1S1Y2Q0NDDBkyJM9tX758CYFAgNDQ0ELnm92Sk13P8fHxMDMzQ9euXQud1okTJ8DhcDBt2rRCbXfz5k3ZbV1XV1esWrUKkydPRu/eveHm5gZra2vZg3LGxsbw9/fHpk2b8OHDh0KXsTy+Pnz4gICAABARGjRogF27diErKytHHV++fBnVqlVDq1atIBKJVFYWkUiEAQMGQCAQ5LkPJSQkYO3atahXrx6ICA4ODli/fj3S0tJw8+ZN8Hg8zJw5U+3fa2RkJCpXroz69esjLS1NJfXWrVs3WFhYoF+/foiMjERKSkqOddatWwc+nw9PT098+vRJbjpXr16Ft7c3BAIBPD09sXz5crx69SrXevHx8Zg/fz6qV68ua6W1sbGRBb7ZL1NTU4WOYfPmzQOHw0FUVFSO39q1a9ewaNEi+Pr6ws7ODoaGhjnS53A4qF69Onx8fDBp0iRs374d9+/fR2ZmJrKysvD69WtERUVhw4YNmDp1Knr37g1bW1tZK5y7uztCQ0Nx9epV+Pj4QENDA7t37y5WXbx//x6urq7Q1NTMcQFYWl5JSUnYsGGDrCW+UqVKmDp1Kt6+fQuxWIwrV66AiHDixIli5XPu3Dno6enB3d0diYmJcte5e/cuNDU1MWLEiEKnLxQKZc+zXLhwQe3fa2Ff8fHxGD9+PPT19aGpqYnBgwfjyZMnRU5PUSqZEOH06dPUqlUrZSebr7CwMNLT06O+fftSdHQ0bdiwQTZLV0hICHXo0IHc3d1p79699OrVKxo3blyuNFQ1sxcAmjVrFh09epSmTJlC3t7ectebOXMmXblyhQ4cOEBaWloKpb1jxw76888/ycfHhyZNmkR8vmKzDj969IiGDBlCtra2tHDhQjIzM5O7XlZWFgUHB9Pbt29p48aNZGxsTMHBwfTmzRuKiIigatWqFZgXAJo/fz7t27ePQkNDqU2bNgqVUZ4bN27QggULKCYmhrp160bJycl04cIF2rdvHxkbG+e53cKFC+n48eN08OBB0tPTK3L+U6ZMoatXr9KuXbvI1NS00NuvXbuWwsPDadWqVdS4ceN8133//j2tXr2ajh49StbW1jRy5Ejy8PCQO+C9SCSiJ0+e0KVLl+jSpUv0+PFj4nK5ZG9vT/Xq1SOhUEhpaWk5XiKRiIyNjcnY2JhMTExkL2tra2rUqFGJz+505coVsrOzI0NDQ6WkB4COHDlCS5cuJbFYTMOHD6fOnTvnmr4624ULF+j333+nRYsWUYsWLZRShh/t2rWLFi5cSDNmzKAOHTrkuy4Aun79Om3bto0uX75MpqampK2tTZqamrRlyxa5g5mXtKdPn1Lfvn2pV69eNGLECKWl+++//9LMmTNJKBRS27Zt6fr16/T69WvS0tIiFxcXatGiBb169Yo2bNhAnTp1ogkTJhR47AOg0G9aKpXS9evXKSoqivT19alSpUo5XooePyQSCQ0ePJg+ffpEXbp0oVu3btHt27cpLS2NNDU1yd7enqpXr04VK1bM8TI3Ny9S3cbGxtLVq1fp6tWrdPPmTUpPTydNTU1asGABubm5FTq9H2VlZdHs2bPp2LFjFBwcTAEBAWqdAQ4APXjwgA4ePEgnT56ktLQ0cnR0pM6dO1OLFi1yfIcAqGPHjuTk5ERTpkwpUn5nzpyhadOmkb29PS1ZsoS0tbXzXHf79u20ePFiWrlyJTk7O+ebrlQqpQcPHtCZM2fo7Nmz9OHDB1q0aBF5eHgUqZylQUpKCu3Zs4e2b99OiYmJ1KpVK+rfvz/VrFmzUOkoPKGQwiFvIdjZ2clu8+dF2X2rbt++jT59+gD41jr7fR/ZyMhITJ06FcC31tm8bo8UpkX2xYsXcHZ2xu3btwu8qpg0aRKICBs3bsx3vSdPnoDH4+HPP/8sME2RSITJkyeDiDB27NgitSJdvnwZlStXhoWFBS5evCg3j/79+0MgEOS4Ovz48SPq1asHKysruS0bP6YxZswYEBEiIiKUctWXnp6OuXPnQkdHR9aZvaBtYmJioKWlhalTpxYqr+9bZPfv3w8iwj///FPksguFQnh6eqJChQqIiYmRu86nT58QEhICLS0tVKhQAStXrszVgl/Q6+3bt/j777/h6+uLX375BQ4ODmjWrBnat2+PHj16YMiQIRg+fDi6d++OFi1awM7ODubm5rKW3Z49e+bZ4qCKV/YQMU2bNkV6enqx03vw4IGs31+PHj0QGxtbYB1nZmaibdu2sLGxydXyp4zX6dOnwefzMXLkyCJ9niFDhqBy5cq4cuVKidWLIq85c+aAw+Hg3LlzxU4rNTUVI0eOBBGhZcuWePPmTY7vIDQ0VPaAUvaDNYoc+368s1JSrydPnkBfXx86Ojrw9PTEzJkzce7cOZW0YH//Sk9Px5kzZ/Dw4UOlpisSiTBt2jTZuMPK2FcL8/r06RNOnz6N0NBQ1KlTB0QEa2trTJkyBc+ePcu3nidMmAATE5NClzn74WMOh4Pu3bsrdNczKysLrVq1QuXKlZGQkJDrt/jx40dERUVhxIgRsLS0lHUTGTp0KC5dulSi36kqXykpKVixYgVsbGxARGjRogW2b9+ebx0IhUIcOnQI/v7+CvfxVUkgGxUVBQcHB4SGhuZaJhaLsXfvXrRp00bp+Y4bNw5ubm7o2bMnhEKhbEIEkUiEfv36wc3NrVAdvvPrI5uYmAhbW1sYGhrKnU0s2+rVq0FEWLBggUJ5DhgwABUrVsy3I79Y/K1PX2HSzUt8fDxcXV2hoaGBv//+O8eyRYsWgYiwefPmXNu9e/cOVatWLbCvbfZBr7gPssnz5s0brFy5UuGHn8aOHQt9fX08fPhQ4TzE4m+39j9//gxLS0u0a9eu2BdhHz58gIWFBZo3bw6x+H+3TzIzM3ON5vBjnzdVE4lE2Lp1K3R1dVG7du0SGeFi/fr1soBTQ0MDwcHBRUpHJBIhKioKw4cPh4aGBmxtbXHy5MkCt8uuY7FYLBuLOK8h1RISEtCvXz/UrVsXkydPVvi39Pr1a9lYjeWtH7NYLIarqyuqVatWrN/r3bt3Ua9ePQgEAvz111/5PsgWExNTqP7M39dxSfv8+XO5mxRly5YtEAgEcHNzU0r3wKysLHz58gUxMTF4/Pgxbt26hYsXL+Lw4cOYN28eunbtil9++UXW/UJHRwf+/v44efJkjjrNr57v3r0LIsKRI0cULpdYLMbw4cNBRJg4cWKhRnWIjY2FsbExbGxsULduXVhaWkJPTy9HNxJLS0uMHDlS1lBUXmWfV9zc3EBEMDc3R0hICJ49eyZb5/Hjx5g4cSIqV64se1Azv9jqeyrrI/vgwQO4ublh2LBhyMzMhFAoxNatW9GiRQs4Ojpi2bJlqspaaQp62OvcuXPw8vICn8/Hhg0bci2PjIwEl8vFyJEjFQ5+Xr58me9T9vHx8ejSpQu4XC4iIiIU/iz5EQqFCA4OBhEhMDAQmZmZOHjwIDgcDiZNmpTndk+fPoW5uTmaNGmClJQUxMXF4fTp01i+fDmGDh0qGwpHWcORFFdCQgKqVasGDoeDHj16KHQizD4wDh48GHp6ekUa7UGec+fOgcvlYurUqZBIJNi6dStsbGzA5XIRGBiY55OqJeXRo0eoV68etLW18xxn9PXr11i0aBG6dOlS5I792fvI4MGDIZVK8ffff4OIFL4S//r1K7Zv346ePXvKnv6vVKkSpk+frvCDkz+e/CZMmAAtLa0cg5tLpVJs2rQJpqamMDY2Rq9evWT5OTg4YOHChTkeepJKpfjy5QuePHmCS5cuwcHBAdWqVVPLE70l4fnz59DV1UVgYKDC26Snp+Pff//FqlWrZHd+6tWrh3v37im9fOoMZMurixcvombNmiAitG/fHteuXStSOgcOHJDdXZP30tfXh7u7O8aMGYPNmzfLWpnlya+epVIp6tSpI7tzW5DU1FR4e3uDx+PlauRR1IULFzBw4ECMHj0a06ZNw+LFixEREYFdu3bh2rVrZX64s6J48OABRo0aJTt+enp6omnTprJnPIYNG4abN28WqsFIpQ97xcXFoUOHDvD29oabmxucnZ2xevXqMjNDjCKjFmRkZMhmx/l+0OHLly9DS0sLfn5+hT54BgYGwtzcHKmpqbL3kpOTMX36dOjq6sLY2Bj79u0r+gfLw/r166GpqYlGjRpBT08PnTp1KnBHi46Ohr6+fo6naQUCAerXr4/u3bvLDfDVKTMzE6tXr5Y9IFVQQCsWi7FmzRoQEVatWqXUsmTfks1+qMfHx6dQrcWqlpaWJntIasCAAUhLS8ObN2/w559/okmTJiAiaGlpQUtLq1DjM2eLioqCpqZmrn1k0KBB0NTUxPXr1/Pc9s2bN+jYsaPsQRwHBwdMnToV169fL/TJ4ceTX0pKCipXrozOnTsD+NaNqHXr1rJW4+xZdTIzMxEZGQk/Pz9oamqCw+Ggdu3asLCwyPWAkIGBgdyhmsqT7IuQQ4cOyV0ukUiwb98+BAQEoH79+uDxeLIHlBwcHDB58mSlDFclDwtkVUMsFmPr1q2yIa7atm1bqFkZDx48CA0NDXTs2BG7d+/G0aNHcf78edy4cQP//fcfYmJiCrU/F1TPM2fOhL6+foEjCsTHx8vOg0ePHlU4f0Zx6enp2LRpE5o3b462bdti586dRd7/VRbIpqSkYM2aNXB2dkb9+vXRoEEDPH78WFXZqYSiw29JpVLMnz8fRN+mj7tz5w5MTEzg7u5epIp5/fo1NDQ0sGDBAmRlZWHVqlWoUKECNDU1ERISotKB269fvw4rKyv8+uuvOQLp/ERHR2PBggXYv38/nj59WiZunf4Y0Pr7+2PHjh14+vRpjgNncnIyrK2t4erqqvSrZ4lEAj8/P7i6upbqyRI2bNgAbW1tmJqaygbU79SpE7Zv347k5GRMmzYN2trahRoj9+bNm9DX10fr1q1zTSGcmZmJJk2awMrKKtdUjFKpFGvWrIG+vj4sLS2xcuXKYreSyzv5bd26VTZ+cfasVvndkkxMTMSGDRswYsQIzJgxA6tWrcKuXbsQFRWFhw8flngXEXWQSqVo3749KlasmGOUGrFYjJ07d8ou2OrXr49BgwZh9erVuH79usqC1++xQFa1xOJvw17Z2dmBiODl5VXgHa9Dhw5BQ0MDnTt3VtrY2AXV8+PHjwscOlEoFMLe3h6VK1eWjXbElG4qCWSXLFkCJycneHp6YufOnUhLS8OECRPg4uKiklaJa9euwdnZGc2aNYO/v3+unSIqKgpWVlbw8PBAy5YtFU63sOPI7tixA5qamuByubCzsytWwDl06FCYmJigRo0a4HA46N+/f4FzICtLenp6iZxcSoPMzEysWbMGNWrUyNF65uHhgTFjxqB79+4QCASlqqVUHe7fv49hw4Zh27ZtuYKyz58/y8YOVMTjx49hZmaGxo0b53l3JjY2FhUrVoSHh4dsf3716hU8PT1BRBg0aFCO2W6KQ97JTyqVolmzZuByuRg9enSZuYukbvHx8TA1NUXnzp1lrXXZD+V4eXnh4sWLaikXC2RLhkQiwe7du1GjRg1oaGhg6tSpcs8lR44cgUAgQKdOnZQ6wYsi9dywYUN07do1z+WzZs0Cj8djQWwZopJAtm3btrJx9r63ZMkSODg4KNyBV1FxcXGyWwUTJ07MNU1pVFQUxo4dW+h0izIhwqVLl9C5c+diB51v376Fvr4+2rVrV+5vSZYWHz9+xIkTJ3I9XDBq1Ch2AizAxIkToaurW2Af0E+fPqFKlSqoW7dugeteuHBB9pT/qlWroKuriypVqhR5Qo685HXy+/TpU5m7i1QaZM/Dnv009m+//Vao282qwALZkpWRkYFp06bJHrr8firkY8eOQSAQwNfXV+kPwSlSzwsWLIC2trbcuyT//fcfBAJBnmPNM6WTSsaRRT7j9e3atYtmz55NEyZMoN69eys7a5o+fTo1aNCAOnfuLHvv3LlzFBAQQFZWVuTn50ejRo2Su21WVhZlZWXJ/k9OTiZLS8t8x5G1t7cnHo+n9M9B9G1s0NIwVuTPLCUlhZ4/f07169dXWT2XBx8/fqRffvmFRo8eTbNmzZK7DgDq2rUrXbhwgaKjo8na2rrAdFetWiXbX4cMGULz588nfX19pZZdIpHQ/fv3Vbov/2zGjRtHr1+/pokTJ5Kjo6O6i8PqWE0ePXpEwcHBdPHiRerVqxe1a9eOBg4cSK1bt6Zdu3aRQCBQan6K1PPr16/J1taWNm/eTD169JC9L5VKycPDgz5//ky3bt1SeCx3RnUU3VdVEsgW5Pz58zRmzBi6deuWUtN98+YN+fv704ULF3IEgKmpqbIdxtfXl0JDQ+UOtKuqCREY5mewZMkS2r9/Px0+fFhusLl//34KDQ0t1IQDAGj37t1UvXr1UhEQMQxTOADo0KFDtGzZMkpKSqJmzZrRggULlB7EFsaAAQPI0NCQlixZInsve7KStWvX0q+//qq2sjH/o+iECGoJZImIHj58SHZ2doXa5v379+Tv75/r/R07dpCOjg516NCBwsPDqVatWnmmERYWRlpaWjRgwIBcy0pbiyyjfqwlR3Hx8fFka2tLkyZNosmTJ+dY9vTpU3J0dCR/f39au3atmkooH6vj8o/Vsfp9/PiRDh48SL179yZNTU2V5KFoPa9YsYJCQkIoLi6OjI2NKSYmhuzt7alnz54UFhamkrIxhVeqW2SVTSwWk4+PD40dO5Y8PT1zLU9OTpYFoj179qShQ4eSu7t7gekmJyeToaFhvoGsg4MDOzCWYxKJhO7cucPqWUEjRoygbdu20evXr2WtsiKRiFxdXenr1690+/btYk0RrAqsjss/Vsc/B0XrOT4+niwtLSkiIoICAgLI29ubbt++Tf/995/SpshmSo78ScfLmO3bt9O1a9do9uzZ1Lx5c9q5cycRfetTR/TtlkHjxo2padOmZGlpqVAQyzBM4YWEhFBKSgqtXr1a9t7MmTPp9u3btG3btlIXxDIM8/OxsLCQxQo7d+6kI0eOUFhYGAtiy6hy0SKrKqxFlmEtOYU3ePBgOnDgAL169Ypu3rxJzZs3p9DQUJo0aZK6iyYXq+Pyj9Xxz6Ew9bx27VoKDg4mY2Njat68Oe3evbuESskoW7lokWUYpvSYOHEiff78mRYuXEh9+vQhNzc3mjBhgrqLxTAMI9O5c2ficDgkFotpxYoV6i4OUwx8dReAYZjypXr16tS7d2+aOXMmGRoa0ubNm1krGMMwpYqZmRnNnj2b6tatS5UqVVJ3cZhiYIEswzBKN2nSJDpy5AitXLmSqlatqu7iMAzD5DJx4kR1F4FRAhbIMgyjdDVr1qT379+zlliGYRhGpdjDXvko6GEvhmEYhmEYRn1YIJsPAJSSkkL6+vp5TrnLMAzDMAzDqAcLZBmGYRiGYZgyiQ2/xTAMwzAMw5RJLJBlGIZhGIZhyiQWyDIMwzAMwzBlEgtkGYZhGIZhmDKJBbIMwzAMwzBMmcQCWYZhGIZhGKZMYoEswzAMwzAMUyaxQJZhGIZhGIYpk1ggyzAMwzAMw5RJLJBlGIZhGIZhyiQWyDIMwzAMwzBlEgtkGYZhGIZhmDKJBbIMwzAMwzBMmcQCWYZhGIZhGKZMYoEswzAMwzAMUyaVm0D2+vXr5OLiQu7u7tSjRw8SiUSyZefOnSNra2tq3rw5eXp6KpwmAEpOTiYAqigywzAMwzAMUwzlJpC1trams2fP0oULF8jGxoYOHDiQY3n37t3p3LlzdObMGYXTTElJIUNDQ0pJSZG7/N69eySVSotV7vKurF8ESKVSVs/lHKvj8o/V8c+B1fPPia/uAiiLhYWF7G+BQEBcbs4Yfe/evXTt2jXy8/OjUaNGyU0jKyuLsrKyZP+npqbmm6dIJCKxWEw8Hq8YJS+/li5dSuHh4RQZGUk1a9ZUd3GKRCKRsHou51gdl3+sjn8OrJ7LF0XrkIOy3mT2gzdv3pC/vz9duHCBNDQ0iOhbQCoQCIiIyNfXl0JDQ6lRo0a5tl2xYgWtXLlS9r9EIqEXL15QUlISGRgY5Fo/OjpaRZ+i7BOLxeTj40OfPn0iAwMDWr58OdWtW1fdxWIYhmGYEpGWlkZpaWlUoUIFdRelTJIXp8lTrgLZ5ORk6tChA4WHh1OtWrXkrhMWFkZaWlo0YMCAXMt+bJFNTk4mS0vLfANZe3v7cnflFxcXRzwejypWrFjkNI4cOUK+vr505MgRmjlzJv3333+0d+/eQvVRLg0kEgndv3+/XNYz8w2r49JLIpGQUCgkHR2dYqfD6rj8K031DIC8vb3pyZMn9PTpU+JwOGotT1mkaB2Wm64FYrGY/P39afr06bmC2OTkZFkgeunSJRo6dKjcNAQCgazllogU6mfD4/HUvsMoW/fu3cnQ0JCOHTtW5DTWr19PDg4O1K5dO/Lw8KAuXbqQt7c3bdmyhbp27arE0paM8ljPTE6sjkufWbNm0ZYtW+jZs2e5uosVBavjn0NpqOfDhw/T8ePHiYjo5s2b5OzsrNby5EcsFtOBAwfIy8uL9PX11V2cQlPbw15hYWFKTW/79u107do1mj17NjVv3px27txJQ4YMISKiXbt2UePGjalp06ZkaWlJ7u7uSs27PPnw4QP9+++/dO7cOcrIyChSGvHx8XT48GEaNGgQcTgc0tXVpYMHD5Kfnx91796dVq9ereRSMwxTGG/fviWhUKjuYuRLIpFQREQEvXz5kq5fv67u4jCMwoRCIY0ZM4Y8PT2pYsWKtHv3bnUXKU8JCQnUpk0b8vPzo+bNm1NCQoK6i1RoJdIiu3Dhwhz/A6A9e/bIHqYKCQkpdh59+vShPn365Hive/fuREQ0aNAgGjRoULHz+BkcO3aMAFBmZiZdvHiRvLy8Cp3Gxo0bSUNDg3r16iV7TyAQ0ObNm8nMzIyCg4Ppw4cPNH36dHa7hWFKWHJyMtWpU4fs7Oxo//79VLlyZXUXSa7Tp09TXFwcaWpqUmRkZKlu0WKY7y1dupRevXpFBw4coLCwMNq9ezctWrRIKXcVlCn7AfisrCwKDw+nKVOmkKurK508eZKqVaum7uIprES+1ePHj9P79++pZs2aVKNGDapZsybxeDyqUaMG1ahRoySKwCjo8OHD1KRJE6pcuTKdOHGi0NtLpVKKiIigrl27kpGRUY5lXC6Xli5dSnPmzKGZM2dS9+7dCxwZgmEY5Tp+/Dilp6dTTEwMOTk50Y0bN9RdJLk2btxIderUod69e9O+ffvK/FB+zM8hLi6OZs+eTSNGjKC6detSt27dKCYmhq5du6buoskAoDVr1lCzZs3I2tqabt26RYMGDaIrV64QAGratCndu3dP3cVUWIkEskePHpWN8/rrr79Sp06dSFdXlzp16kSdOnUqiSIwCsjKyqKTJ0+St7c3eXl50cmTJwudxvnz5+nFixcUGBgodzmHw6FJkybR3r176dixY+Ti4kIvXrwobtFLjc+fP+d4YLAgCxcupKVLl6quQAzzgwMHDlCDBg3o1q1bZG1tTc2aNaNt27apu1g5JCUlUWRkJPXr1486d+5Mz58/p4cPH6q7WAxToAkTJpCOjg5Nnz6diIhcXV3JwsKCdu3apeaSfZORkUEBAQEUFBREQ4YMoXPnzpGlpSUREVWvXp0uX75MFhYW5O7uThcuXFBavm/fvqW0tDSlpZcDStDr168RFBSEhQsXonnz5iWZdZEkJSWBiJCUlCR3+c2bNyEWi0u4VKpz+vRpEBFu376N7du3g4jw7t27QqXRo0cP1KpVC1KptMB1Hz58iBo1asDIyAjHjh0rarFVSiwWK1zPDx8+hKGhIYYPH65Q2hkZGdDX14euri6+fv2q0DZXr17Fzp07FVqXUUxh6risy8rKgpGREaZNmwbg22+wT58+ICL88ccfkEgkai7hN+Hh4eByuYiNjUVmZib09fUxc+bMIqdXnus4KysLIpFI3cUoFdRdz5cvXwYRITw8PMf7I0aMgKWlpdr3rwcPHsDBwQHa2trYvHlznuslJSWhZcuW0NTUxP79+4ucn1gsxsGDB+Hl5QUiQr9+/YqcVn5KNJDNdvr0aSxevFgdWRdKaQxkMzMzMWfOHPz3339KT3v06NGwtLSEVCrFx48fweFwsGHDBoW3//TpEwQCARYtWqTwNl+/fkX79u3B4XAwb948hQLgkqTogfHDhw+wsbEBj8eDsbExMjMzC0x77969ICLw+XyFvjOxWIxatWpBU1MT79+/V/gzMPlT98mvJJ05cwZEhJs3b8rek0qlWLhwITgcDnx8fJCcnFzsfObOnYtx48YV+cTt5uYGLy8v2f/+/v5wcHAocnmUWcd5nQ/UQSKRoFmzZnB1dUVWVpa6iyOjruO4OvdlsViMRo0aoVGjRrnyv3jxIogIly5dKvFyAYBQKMSsWbOgoaGB2rVr486dOwVuk5mZCT8/P3C53FyBeUE+f/6MRYsWoVq1aiAiNG7cGN26dYNAIMDHjx+L+jHypJJA9tmzZ1iyZEmp2uGLoqQC2ejoaAiFwgLX+/z5M9zd3UFEsLKyQmxsbLHz/l6NGjUwePBg2f+Ojo7o0aOHwtsvXboUGhoa+PDhQ6HylUgkmDp1KogI3bp1K1UHZEUOjOnp6XB2dkalSpVw9OhREBEiIyMLTNvPzw8NGzZE//79YW1tXWCrys6dO0FE0NTUxB9//FHYj8Lk4WcKZEeNGiW7WP3R4cOHoaenhz59+hQrj7S0NOjp6YGIMGLEiEIHNc+fPwcRYevWrbL3du3aBSLCy5cvi1QmZdXxuXPnoKGhgX379hUrHWVZs2YNiAg8Hq/UHBPOnDkDS0tL3Lhxo8TzVtW+nJWVhcePH+PgwYM4deoUUlNTc60THh4OIsLly5dzLZNIJKhcuTJGjhyp1HIp4saNG6hfvz54PB4mT56MjIwMhbcVi8UIDg4GEWHWrFkF7stJSUkIDg6GlpYWBAIB+vbti2vXrgH41tClqamJBQsWFOvzyKOSQHbcuHEIDg6WuywzMxPPnz9XRbZKVxKB7Llz50BEsLe3z9FK8qMXL16gVq1aMDU1xZ49e2BtbY369esr7WLhyZMnICIcPHhQ9t6kSZNgZmamUKuKVCpFvXr14OfnV+Qy7N27FxoaGujbt2+paZkt6MAokUjQrVs3aGtr4/r16wAABweHAr+H5ORkaGlpYeHChbhz5w6ICDt27MhzfYlEAnt7e3h5eWHs2LEwNDQs8xeKpcXPEshKpVJUq1YNQUFBea4TEREBIsL58+eLnE/2BdfEiRNBRJg0aVKhtp82bRr09fWRlpYmey8lJQWamppFvpOnjDpOTU1F9erVQURwdXUtcjrK8v79exgZGSEgIADz5s0Dh8PB6dOn1V0stGzZEkQECwsLxMTElGjeytqXL1y4gLFjx6JDhw6oUaMGeDweiEj24vP5cHZ2xoQJE3DkyBG8efMG5ubm6N27d55pjhw5EpUrVy6x7gXp6ekICQkBl8uFg4MDbt26VaR0pFIpQkNDQUQYOnRont/t+fPnUbVqVejp6WH27NlyG7T69euHatWqKf1Yq5JAtnXr1rh69Wqey3v06IE1a9aoImulUjSQFQqFuHTpEkJDQ9GqVSv88ssvePPmjUJ5tG/fHjVq1ICDg4PsqvrHK6br16+jQoUKsLW1xdOnTwEA9+/fh6GhIVq3bq2UFsy//voLWlpaOU4e58+fz3UbMi///vsviAjHjx8vVjmy++ZOmDChWOkoS0EHxkmTJoHD4WDv3r2y9/78809oamrm2+918+bNICLZ78TT0xONGzfOM4CPjIwEEeHixYuIjY2FhoZGobpwMHn7WQLZe/fuFbiPSiQSODs7w87OrsjHFV9fXzg5OQH4ti8QEebNm6fQthKJBDY2Nhg4cGCuZd7e3kUOIJVRxyNGjIC2tjbmz58PIipyYKAsffr0gYmJCT5+/AiJRAJPT09YWFggISFBbWXKvihfunQprK2t0bBhQ7mtl6qijHo+ffo0NDQ0YG1tjXbt2mH06NFYvXo1zpw5g9jYWDx8+BBhYWHw9/eHhYWFLLjV1dXN95mSS5cuyY7hyvT+/Xtcv34dkZGRWLlyJf744w/069cP1atXh6amJubOnauUGGHdunXg8Xjo2LEj0tPTZe9nZmYiJCQEHA4Hbm5u+d41uXbtGogIR44cKXZ5vqeSQNbe3h5xcXF5Lj9w4AC6deum9HxDQkLg5uaG3r1756g4sViMgIAAuLm5YdSoUQqnl18gKxQKMXz4cHh6ekJbWxtEBH19fbRv3x4mJiY5btHn5eHDhyAi/PPPP8jKysLs2bOhoaGBOnXqyC4E9u/fD21tbbi4uOTqW3LmzBloaGigf//+xW7BbNmyJdq1a5frM+rp6WHOnDkFbj9w4EBUrVpVKVebS5YskR0M1S2/A+OGDRtARFi4cGGO92NjY8HhcBAREZFnur/99luOk/Lhw4fzvC0llUrRqFEjeHh4yN4bMGAAKleurFBfXCZ/RTn5KeuOwefPn5WSjiJCQ0Ohr69f4G/m1q1b4HK5RWr9/PLlCzQ0NHLsu9OnTwcRYcWKFQVun32H6sKFC7mWrV+/HhwOB/Hx8YUuV3EDnOxyLV26FCKRCNbW1ggICChSWspw9uxZEBHWrVsne+/du3cwMzNDhw4d1HZH6/tuUnfv3oWuri46duxYYq2Qxa3nO3fuQF9fH23atFEo+JNKpXj27BkiIiJw6tSpfNeVSCSwtLTEiBEjilQ2ebZv3w4OhyMLpjU0NFC1alU0bdoUvXv3xqNHj5SWF/DtPKWtrQ1XV1d8/vwZ9+/fR4MGDaChoYH58+cX+L1nn8vat2+v1HKpJJB1c3PD3bt381z+6tUrNGnSRKl53rlzB7169QLw7YC9bds22bL9+/dj8uTJAIBBgwbhypUrCqWZXyD74MEDVKpUCb/99hsWLVqEGzduyPo4Lly4EHw+H69evco3/YEDB6Jy5co5+sfev38fTk5O4HK56NixIzgcDrp06ZLjCuh72S17M2bMUOgzyZOYmAg+n49Vq1blWubt7Z0jgJInOTkZurq6xXqq+Efjx48Hh8PJ93Z7ScjrwBgVFQUNDQ0EBgbKPWl4enrmOTLHp0+fwOfz/6+9+w5r8lz/AP4lIQGUoSJLFCtowY0g4gDFvat1AFKWVKVWe+quW6siehxtcRX1qNXaodUqbVVctcfRqogidVZO1TrAhSwFMu7fH155f8YESCCICffnunJd8M4nuTPu93mfQatXrxaWKRQK8vT01NokQdXu9uXbhleuXCkzWWa60ffHb+bMmeTm5kY///xzhc776aefkkgkUqvNL82DBw9o7NixNGjQIAoKCiIfHx9q3LgxOTg4kKOjo9bk72V+fn40fPhwnc41fvx4sra21rsdvmq0gZcrMpRKJU2aNIkAlNl5NDo6mtzd3bV+ph4+fEhisbhcd/MqkuComhQEBAQICdnixYvJwsKiUjqulKWwsJA8PT3VyqPy008/EQBKSEh47eW6f/8+SaVStQv7pKQkMjMzo+nTp7+WMvz444904MCBcsX55s2b5OLiQr6+vgbp8KjNhAkTyMXFxSB3fx49ekR169alwYMH0/nz5ykrK+u1XDD88ccfZG9vT40aNSKpVErNmzen8+fP67z/f/7zHzIzMyt3e3dtKiWRnThxIs2bN6/E9bdu3apQD1Rt1q5dS1999RURvbjlP27cOGHdtGnThDZfP/zwQ4m3ZIuKiigvL0943L17t8ymBUVFRSSXy9UeOTk55OjoSDExMRrrVI9//vmHpFIpxcfHa6wrLCykJUuWkJWVFU2cOJGKi4tLPI5cLqeFCxcSANq4cWOp25X0ULVpy8jI0FiXkJBAEomEsrOzS9x/3bp1JBKJ6O+//y7X+bU9iouL6b333iOpVEoHDx402HH1fRQVFWnEOTMzk+zt7al79+70/Plzrfv95z//IQBaXxPV63Xv3j215WvWrCGRSER//fWXsEwmk1H79u3J39+fZDKZ2vaDBg0iT0/PMt8f/NA/xiU9VBcVnp6eBIDCw8PpwYMHep9zz549BIA8PDzIwsKCjhw5Uur2WVlZ1KpVK7K3t6d+/fpRaGgoxcbG0pQpU2jBggXk4+NDLVu2pMLCQq373759mwDQV199pVP5Hj16RE5OThQcHKzX8woKCqLu3btrLJfJZDRmzBgSiUS0efNmjfeyXP7ie9Pa2prmzp1b6vF79epVqTF+9TF+/HiysrKiq1evCssyMzPJwsKCFi1a9NrfrwsWLCBzc3O6cOGC1vUfffQRSaVSOnfunNrywsJCOnDgAI0ZM4YiIyPpzJkzOp3v1KlTtGPHjjK3mzVrFtWsWZMePXqktnzZsmVC7bG2/R4/fkx//PGH1veEPo8rV64QAPL29qZnz57ptW9WVhZ5eXlRo0aN6O7du5UWO9XoBUePHtW6/syZM7Rq1SqdXouYmBiys7OjO3fuvPb34KVLl8jb25smTJhA+fn5eu2bm5tLtWrVoqlTp5a5ra4qZYramJgYhISEoGnTpsI0sS87f/48GjRoYNBzZmdnw8XFBQBgZ2eHJ0+eqK2ztbXVuu5liYmJWL16tfC/QqEo87zp6elal7/33ntISEjAO++8g/r162usX7NmDSQSCTp06IALFy5orO/RoweCgoJgbm5e5gwbffr0QWpqKmJjY5GamooBAwbA0dGxzLKrbNu2DR4eHsjOzkZ2drbauvr160Mmk2Hz5s3o3Lmzxr65ubmYPXs2unbtisePH+Px48c6n7cs48ePR0ZGBt59912sX78enp6eBju2vl6O8/z58yGTyfDJJ5+UOEi7h4cHLCwssHLlSkRFRamt27hxI/z8/HDv3j3cu3dPWN66dWvY2Nhg3rx5mDRpEgDg7Nmz+OOPP/D5558jLS1N7TiDBw/GyJEj8cUXX6Br1656PZ+UlBSsWbMGEyZMQOvWrcvc/vbt20hOTkZERAQsLS31OtfLTp8+jbVr1yI4OBj9+/cv93EqQ0mfZZXs7GxERESgffv2SEhIwC+//IIVK1Zg//79mDFjBoKCgnQ6z+3btxEZGYnOnTsjPj4eEydOxKBBg7BhwwatMx3m5ORg7NixePjwIRITE+Hu7q6xTaNGjRAZGYn58+dj2LBhGut37doFsViM+vXra/2+0ebDDz/EvHnzEBQUhHbt2pW5/YMHD/Dbb79hzpw5Ws8xatQo3L9/HyNHjsSGDRswbdo0te/GX375Bfn5+fD19S2xjH5+fli5ciWOHz8OGxsbnZ7Hy8qK8avOnTuH1atXY/LkycjPz1crV69evbBq1Sr06NED5ub6/5RmZmZCJpPB1dVV56lL79y5g7i4OISFhUEul2t9nUJDQ5GcnIyhQ4diy5Yt+PPPP3H48GH8+uuvyMnJQb169WBmZoatW7fC398f0dHRaNu2rdp04YWFhTh06BB27tyJy5cvAwCWLFmCHj16aC1XYWEh1qxZg/79++PmzZtq64KCgvDuu+8iNjYWSqUSDRo0wIULF3D+/HmcP38eN27cgFKpxIwZMzB06FDdXjwt1qxZA0tLS6SlpWHmzJkIDw/Xab/CwkJ8+OGHyMzMxKZNm3D//n3cv3+/3OUojYWFBZycnLBu3TohJwFezIi5fft2rFmzBnK5HJcuXcKoUaNKPE5qaio2bdqEGTNmIDMzE5mZmZVS3tJs3LgRAHD16lW99+3Xrx82bNiAwYMHw8LCosTtfH19dTugzimvnr7//ntq2rQpRUdH06FDh+ju3buUnZ1Nhw4dooCAAEpMTDTo+dasWVNijezUqVNfa42sXC6nvLw8cnZ2psjISI11T58+pdq1a9OECRMMdoVUWFhIsbGxZGlpSSKRiHr16kXbt2+nvLy8UvcrLi4mBwcHmjZtmtb1MpmMGjVqROPGjdO6Pjo6mmxtbenWrVuVcuWXnZ1Nvr6+5OjoSLt27arwVbu+j1drcg4ePEgAaP369WXuGxwcTC1btlRbdvv2baFJgLZ9PvnkE7KxsaEnT56QXC6nLl26kI+PT4nPu3PnzuTn56f36xIYGEhisZjEYjHNnTu3xJq8oqIiWrFihdAOPDo6ulyv46NHjygmJoYACGMLLly4sNzxLCoqouDgYProo48oJyfHoDHW9pDJZDRgwACqW7cu/fPPP2rxHDBgAAGgkJAQyszMLPVcT58+pebNm1OTJk3o8ePHJJfL6cmTJ+Tj40MuLi5048YNte0fPnxIPj4+VLdu3RJr4FSPkSNHkr29vdYa4r59+1KXLl30el1kMhkFBASQp6cnFRQUlLn9smXLyMLCQnheJR1z9+7d1LBhQ7KwsKA5c+YI31HdunWjwMDAUs9x8+ZNAkBbt241eIxffeTk5JC7u7swRuur68+ePUsAdKqtfPWxZ88ekkqlQichf39/Gj16NCUkJNCvv/5KmZmZGp8NmUxGvXv3Jjc3tzLf8+np6WRlZUUSiUSo+Z82bRqdPn2aZDIZFRYW0jfffEPe3t4EgNq2bUs7duyga9eu0eTJk6lOnToEgHr37k179uyh4cOHk62tLV2/fl3r+RITE8nMzEyt1vrlx/PnzykoKIjMzc2F9pweHh4UFRVFGzZsoLCwMLKxsaGbN2/q/Vqq4uvq6kqjR4+m8PBwsrCwoIsXL+q036BBg6hGjRp06tSpcp1b38eECRPIyclJeC/euXOHevbsSQBo0qRJNHPmTAJA3377rdb9CwoKyMvLizp06GC0d+MuX76s0x0iXVXqhAhnz56loUOHkqenJ3l5eZGXlxd5enpSTEyMTuOm6uP8+fPC+IdxcXFqbWR//PFHmjNnDhERjR492iBtZInKHn7riy++IJFIRNeuXdNYbm5uTrdv39apHPp4+vQpbdiwgTp16kQAyM7OjsaMGSOMdvAq1WgDpfWkjI2NpSZNmmgsT05OFpK6yvTgwQPq168fAaABAwaU2fbYkOTy/29b9/z5c2rSpAkFBgbq1BYpKSmJAKi1F//ss89IKpWWOKLB3bt3SSKR0MqVK4XbUKWNWam61f3rr7/q/JzOnTsnDPc1d+5cEolE1KlTJ43X9dq1a8L76F//+hetW7eOANCmTZt0PhfRi9ehXr16ZGtrS+vXryeFQkHz588XhnMpz6xEcXFxJBKJyMrKipo0aVLqKClEL9oUR0REaG1L/nKMS7J27VoCQHv37tVYp1Qq6euvv6Y6depQ3bp16csvv9R6LKVSScHBwVSzZk36888/1dZlZmaSh4cHvf3220Kv8+zsbPLz86M6deqU2udA5f79+2RjY6MxVqVq6KqVK1eWeYxXXbx4kcRiMS1evLjMbX19fWnIkCE6HbegoIBmzpxJEomE3N3daePGjWRmZqbWeakk7dq10/k8KrrE+GVKpVJoUvDXX3+VuF1AQAB1KaMPwauSkpJIIpHQ4MGD6cCBA7Rs2TKKiIgQOs2oEr1atWqRn58fhYWF0bx582j27NkaQySWdZ45c+bQ+fPnS+z8pVQqKTk5mbp27ap23kmTJqk976dPn5K7uzu1bdtW47dbqVRS8+bNadCgQaWW58mTJ7RkyRL6/vvvNXr3Z2dnU7169ah///7l6qi2f/9+AkC///47nTx5kpo2bUpt27YttcOWTCajUaNGkVgsrnB7d32ofnN//fVX+uWXX8jBwYGcnJwoOTmZiF68nmFhYWRpaUl//PGHxv6qpiXp6emvrcyVoWfPntShQweDHOu1zOx148YNSkpKoh07dujVKFhfU6ZMoYCAAAoLC6OioiJh5ACZTEZRUVEUEBCgV4/Biiayz58/p3r16qmNLSeTyeitt94SOqZVpuvXr9Ps2bPJ1dWVrK2ttU5tOnv2bKpTp06pycTu3bs1BiPPy8ujhg0bUrdu3V5LD1mlUkm7du2i+vXrC0PgaLsYevbsGZ08eZI2btxIBw8epH/++adC5Xv5B3Du3LkkkUh0nlWtqKiI7O3taerUqcKydu3alfmFHx4eTm+99Rb16NGDWrRoUWrSrFQqqVWrVtSnTx+dykT0Ytiehg0bCjE/fvw4NWzYkGxtbWn79u0kl8tpxYoVZGlpSR4eHmpjir7//vtkZWVFFy9eLPM8Dx8+pLCwMAJA/fr10xhTUjWcy8CBA9WGfStLSkoKmZub08yZM+nq1avUrl07EolENGvWLI33xI0bNygyMpJEIhHVrVtXo6c3UdlJzqVLl8jS0rLU8VeJXiSj0dHRBIBatWpFR48eVVuvaiu4c+dOrfvfuHGDHB0dyc/Pj+7evUv+/v5Uu3ZtvYZ5Wrp0KYnFYrp06ZKwTDWDXHnH7540aRJZWVnRzZs3S9zm6tWrBIB++OEHvY597do1oTaqRo0aOnWyWbJkCdWoUUOv90xJMS4sLKSNGzfS9OnTKTw8nLp06SK0W4YOI6eo+hfocqFB9KLjsUQioSFDhmhNsoqLi+nixYu0c+dOWrx4MY0cOZI6depEjo6OBEDvBF4fp0+fpm+//bbE1/Xs2bMkkUho4sSJastVFRrHjh2r0Pn37t2rMRGGroYPH07NmzcnmUxGKSkp9Pvvv5NYLKYFCxZo3f7p06fUt29fEovFtGXLlgqVW19KpZLc3NyEO1N9+/bVGHP1+fPn1LFjR3J0dFT73F27ds1kJsRRDSlpiGHsXtsUtarbOsbEEBMiqDrwqJKf7777jgBUakL/qtzcXAoJCRFq1l6Og7e3N4WFhZW6f3Z2NonFYrXmIKraioyMjEortzZ5eXk0efJkEovF1KxZM/rxxx9pw4YNNGbMGGEsXlXNguphY2ND7dq1o+joaFq8eDHFxcXRzJkz6eOPP6bRo0dTWFgYBQcHa63VVP0Apqenk0QiEWr2dfXhhx+Sq6sryeVyysjIKHPiA6L/rzFV3V4qy9dff00AdJp28N69eySRSDSGVsrOzqYRI0YQAHJzcyMzMzOaMGGCxo/as2fPqFWrVuTp6Vlq0vHTTz+Ro6Mj1alTh7Zt21bixcS+ffuoRo0a5O/vr9P4lwUFBeTp6Uk+Pj7C+1gmk9HChQvJ3NycvL29KT09nW7evCnUtri4uNDq1auF5jcSiURtmLPSEtnCwkJq3bo1NW3aVOfE6cyZM9ShQwch8cjIyKDDhw+TSCQqc3zk1NRUsrGxISsrK6pVq5ZOYzi/Wl4PDw/q2bOn8JpHRUVR8+bN9TrOy3Jzc6levXrk7+9f4pig8+bNI1tb2xJHVymNUqmk3bt3a73Q1kY1eYsus+epaIuxajITkUhEDRs2pICAABoxYgRNnTqVEhIS6MCBA2VeBBcXF5OrqyuNGjWqzDLs2rWLzM3Nafjw4eUa1zMnJ6dcdy8M6fPPP9e4M9G7d29q06aNQSo0QkJChOYxulJNjb5ixQq1OM+ZM4fMzc3p3LlzattnZGRQs2bNqFatWmUOmVVZZsyYQVKplD777LMSKypU0563aNGCcnJySKlUUteuXcnd3b1cn7M3jUwmo/r16+v02SnLa0tk27RpUym30iuTIRLZwsJCatCgAYWGhgpjqPXo0aMyilsqpVJJq1atIolEQu3bt6fbt2/TnTt3CIBaM4ySdOzYUagNOH78OJmZmZXrVqWhpKWlCcmCSCSili1bUkxMDH355Zd07tw5ev78uXAnYOnSpRQVFUV+fn5Uu3ZtcnBwoLfeeouaNWtGfn5+FBQURK1atSIzMzOaMmWK2jibcvmLnqQBAQHUpEkTvab3IyI6deqU0Es1Li6OatasqVNC1LVrV/L09NTpVqiqlj80NLTMbefMmUPW1tb09OlTreu3bdtG3bp1K3Uop2vXrpG1tbXwnn5ZQUGBMKXhgAEDdBrz8+zZs8KEH2WNezhu3DiytLTUWit+7tw5atasGUmlUpJIJOTg4EArVqxQ+9IvKiqiwMBAcnJyEmqIS0tkJ06cSFKpVKeLhJcplUravn07ubq6klQqFSYv0SWeR44coTZt2ggzxelLVbOVlJREMpmM7O3t9Z5d61Vnz54la2tr6t27t9Zby02aNKHo6OgKnUMfzZs3p8jISJ231xbjSZMmaUxmUh6LFi0iKyurUscE3rlzJ4nFYgoJCanyZLQilEolDRo0iGrXrk23bt0SxkLfunWrQY6flZVF9vb2On2XqSQkJJC5uTllZWWpxbmoqIi8vb2pefPmwnf6b7/9Rvb29tS4cWO6evWqQcpcHkVFRTpN5X7p0iWytbWlfv36CSPhqJogmIKFCxeSlZWV0NQuKyuLkpOTaenSpRQWFlbi3atXvbZE1tvbu1omskQkNIRftWpVlb8Rf//9d2rQoAHVrVuXoqOjSSQS6TQo+6effkp2dnaUl5dHnp6e1L59e70aY1cGhUJBFy9epLy8PIMca/ny5SSVSqlVq1bCrXO5XC60TTty5Ijex1UqleTu7k4xMTHUokWLMmu/VR49eqTXwO+qeb5La+v17Nkzqlu3rkEG5FbdWVi3bp2w7Pz589S0aVOytLSktWvX6lVDk5GRQV5eXmRlZUVffvml1n1V7YFfHn/3Vc+fP6e4uDhatmxZie+LrKwscnNzI19fX2GYnlc/y0qlUhijuSIXbPn5+TR37lzq2rUrPXr0qNzH0YdSqaSePXtS48aN6dChQwRAa1s7fR0+fJikUimFhYWp1SKpOj0dPHiwwufQ1bx580gsFlPPnj1p7dq1pc6oRKSZyK5cuVLnSRrKkpWVpTF+qsqzZ89o06ZNJBaLKSwszKiTWJXHjx+Tm5sbdezYkUaOHEkuLi4GvduqusOkrT26Nt7e3jR48GAi0ozzxYsXSSKR0CeffEKbNm0iiURCXbt2fa0TkVRUcnIyicViMjMz0/n3w1jcv3+fzM3NqXXr1lSvXj3hTqS1tTV16tRJ5/cAJ7KlMFQiW1RURG+99ZbQdq6qZl1RefjwIfXu3ZsAUGBgoE77/P777wSAevbsSVKpVK0NnilJS0ujFi1aCLeq7ty5Q7a2tkJHwvKYM2eO0OZO144a+lIqldS/f39ycHAoMQFWdagprfOKPsaNG0dSqZTOnj1Ly5cvJ4lEQq1bt9a5DfGr8vPzKTY2lgDQO++8o3Z78eHDh+Ts7Ex9+vQxyOcnNTWVrKyshOTi5c/y4cOHqV27dgSAgoODX9usRIb0559/klgsJldXV3J2djbYc9ixYweZmZnRv/71LyEOEydOJCcnp9eapBUWFtKaNWuoR48eQk94f39/io+P19qx9eUER3URZshpsCMjI6lhw4Z06dIl2rJlC40dO5Z8fHyEsoWHh1f5hb8hnTp1SmjGpcvMj/pQKpXUr18/qlevXol3jlRSU1PVvle1XZTGx8cLCdLo0aMNMl3r67Z+/Xry8vKizMzMqi6Kwc2fP5/69etHs2bNop07d9Jff/2l9/dVpSayd+/eFb7skpKShFuqSqWyzCvoN4GhElmiF0mEIW/BVJRc/mIAfl3nfZbL5VSrVi0CUGIDelPx/PlzYSai2rVrk52dXYW+QFQdYWrXrl2p7cSzsrLIycmJ+vTpo/FFoOpZ/M477xjsfIWFheTr6yv0tJ48ebJBpszds2cP1a1bl5ydnWn//v2kVCrp3XffJXt7+1KnvtaXKqGJj4+nlJQUOn78uNB729/fX20mNWP00UcfCT/ehqQawSEuLo7kcjm5uLhojJTwOj158oS2bt1KQ4YMoRo1ahAA6t69O+3du1f4flYlOKpa5fDwcINeoKSkpKi1y/fy8qKoqChau3YtnTt3rsorLyrDypUrydnZuVLuNNy+fZtsbGzKfO9+9NFH5OzsLFxEaUtkVZ29V61aZZJxYJWcyHp5eWl9kz958oS8vLwq89QGYchEVi6Xq32xGqOIiAhq06aN0XXaK6/Dhw9T8+bNafHixRWOW5cuXV7Lj/2BAwcIAH322Wdqy1W3mF/tSV9R//vf/6h///4G7zRx//594a6Bqld7RdsyajNz5kwyMzMjX19fAkAtWrSgvXv3msQP3uPHj8nX11etY5uhfPrpp8K4woZqumAIBQUF9PXXX5O/v78wZvGyZcvo4cOH9N1335GdnR316NGjUr7Ddu/eTQcPHixxaD1TVJm/Z6rh/kqa1riwsJDq1KlD06ZNUytPeaciZsarUhNZT09PrW1R7ty5Q61bt67MUxuEIRNZU1BUVKR3ZydjZ6gvRrlc/tpuUU+YMEGjg1L//v2pdevWRpWgKRQK+uKLL8jCwoJiYmIq7RxDhgwhNzc32rZtm1E2I6gKqnFWAZC7u/sb+b46c+YMRUREkEQiISsrK7KzsyNvb+8Sv8/Zm0WhUNDo0aOFu4Cvvsd27NhBANQ6iHIiWz2ZERHBwOLj4wEAW7duxfDhw2FlZSWsUygUuHjxIkQiEb777jtDn9qgcnNzYWdnh5ycHLXp5FTOnTsHb29viMXiKigdex0UCgUuXLhgVHEuLCyEv78/5HI5UlJScPv2bXh5eWHz5s2Ijo6u6uLp7fHjx6hVq1alvf5yuRxpaWlGFeM3gVKpxPTp0+Ht7Y2wsLCqLk6JsrKy8OWXX+LgwYP4/vvvtU4Zzt5MRIS4uDjMmTMHo0aNwtq1ayGRSAAAffv2RU5ODk6dOiVsb4zf16zi9J8gWgequZmJCNevXxfeeAAglUrh5eWFmJgYg53vzJkz+PjjjyGRSODq6oqtW7eqnfPYsWOIiIiAh4cHxGIxjhw5YrBzM/amsbS0xLfffgtfX19MmTIFAODo6IjQ0NAqLln52NvbV+rxX55jnulOJBLh3//+d1UXo0xOTk6YPXs2BgwYABcXl6ouDtODmZkZZs+eDTc3N7z//vu4c+cOdu7ciadPnyI5ORnr16+v6iKyN0ClJLLbtm0DAMyYMQOzZs2CtbV1ZZxG0KBBAxw9ehRWVlaYMWMG9u7di2HDhqltExISguXLl1dqORh7UzRr1gwrVqzAuHHjIJFIMGvWLFhaWlZ1sRhjTG+RkZFwcXHB0KFD0aVLFwQEBMDS0hLBwcFVXTT2BqiURFZF1cSgsr18lS2VSiESiTS22bVrF06fPo1hw4bh448/1nqc4uJiFBcXC//n5+eXeW6FQlGOEjNjoYqvMcZ5zJgx2L9/Pw4dOoTRo0cb5XN4HYw5xkw3HGPj161bN/z2228YOHAgEhISEBERgZo1a6rFlONsWnRtHlIpbWSryq1btxAaGor//ve/ak0L8vPzIZVKAQCDBg3CokWL4Ovrq7H/qlWrsHr1auF/hUKBjIyMUtvIMvYmKyoqQlZWFtzc3Kq6KIwxVmFZWVn4/PPP8f7776Nx48ZVXRxWibTladoYVSKbmZmptZ3fd999hxo1amDAgAHYsGEDPD09SzzG2rVrYWlpqbWN7qs1srm5uXB1dS01kW3ZsiU3KjdhCoUC6enpHGcTxjE2fRzj6oHjbFqqVY2sXC7HO++8g8mTJ6N79+4a63Nzc4VENCwsDB988AE6d+5c5nHLGrWAMcYYY4xVHc3GpEbo22+/xenTp7Fw4UIEBQXh+++/BwDExsYCAHbs2IF27dqhY8eOcHV11SmJBQAbGxvk5OTAxsam0srOGGOMMcbKxyRqZBljjDHGWPVjEjWyjDHGGGOs+uFEljHGGGOMGSVOZBljjDHGmFHiRJYxxhhjjBklTmQZY4wxxphR4kSWMcYYY4wZJU5kGWOMMcaYUeJEljHGGGOMGSVOZBljjDHGmFHiRJYxxhhjjBklTmQZY4wxxphR4kSWMcYYY4wZJU5kGWOMMcaYUeJEljHGGGOMGSVOZBljjDHGmFHiRLYURITc3FwQUVUXhTHGGGOMvYIT2VLk5eXBzs4OeXl5WtdfvHgRSqXyNZeKvU5KpZLjbOI4xqaPY1w9cJyrJ05kK0Amk3FtrYkjIo6zieMYmz6OcfXAca6eOJFljDHGGGNGiRNZxhhjjDFmlMwNdaDr16/j8uXLaNy4MVq0aGGowzLGGGOMMaZVuWtko6KihL9/+uknTJo0CdevX8fcuXPx9ddfG6RwjDHGGGOMlaTcNbI5OTnC31u3bsXmzZvh4OCAgoIChIaGIjw83CAFZIwxxhhjTJtyJ7JEhMLCQiiVSiiVSjg4OAAAatasCbFYbLACMsYYY4wxpk25mxbk5eWhf//+GDBgAJ4+fYoHDx4AAAoKCgw69MUnn3yCwMBAREREQCaTqa07duwYGjRogKCgIHTv3l1YbmNjg6CgIAQFBSE9PR0AcOLECXTs2BEBAQHCMsYYY4wxZrx0rpG9ceMGfv75Z8TExMDW1hZHjx7Vup1IJMLq1asNUri0tDTcvXsXx48fR1xcHH744QeMGDFCbZuQkBAsX75cbZmnpyeOHTumtmzWrFn45ZdfkJeXhw8++AD79u0zSBkZY4wxxljV0DmRTUxMxLNnz2Bra6uxrri4GP/88w88PDxgZWWFBg0aGKRwp06dQq9evQAAffr0webNmzUS2V27duH06dMYNmwYPv74YwBARkYGOnfujObNm+Ozzz4DEUEsFqN27dqoXbs2njx5ovV8xcXFKC4uFv7Pz88vs4wKhaK8T48ZAVV8Oc6mi2Ns+jjG1QPH2bTo2kxV50Q2LS0NCxYs0LpOKpVizpw56NKlC2JjY3U9ZJmys7Ph4uICALCzs9NIQNu2bYtr164BAAYNGoSAgAD4+vrixo0bsLe3x4IFC7BmzRqMGDFCLQE3NzdHcXExpFKp2vESExPVapN1+TBwM4XqgeNs+jjGpo9jXD1wnE2Dr6+vTtvpnMhmZmaiYcOGJa4PDQ3F9u3by5XIZmZmIjQ0VGN5r169kJubC+DFKAl16tRRW29tbS38PXDgQKSlpcHX1xf29vYAgGHDhmHJkiUYO3ascBwAkMvlGkksAMTGxmLkyJHC/7m5uXB1dS217C1btuTObSZMoVAgPT2d42zCOMamj2NcPXCcqyedE1k7Ozs8fPhQqCF9VatWrbB48eJyFcLZ2VmjTSsAXLhwAStXrkRkZCSSk5PRqVMntfW5ublCTeuJEyfwwQcfoKCgAJaWlhCLxTh+/DgaN26MGjVqQC6X4+nTp8jLy9NIiFWkUqlagqtUKsssu1gs5g9MNcBxNn0cY9PHMa4eOM7Vi86jFvj5+WH37t0lH0gkQlFRkUEKpeLt7Q0nJycEBgbi0qVLGDp0KAAItb47duxAu3bt0LFjR7i6uqJz587466+/4Ofnh86dO2Pfvn1Cu9lFixahX79+CA0NRXx8vEHLyRhjjDHGXj+da2RjYmIQEhKCpk2bIiQkRGP9+fPnDdbJ62XLli3TWJaYmAgAGDVqFEaNGqW2ztvbG6mpqRr7dO7cGadOnTJ4+RhjjDHGWNXQOZFt0aIF5s2bh/nz5+PAgQN477330KxZM9SoUQMpKSlYvnw5IiIiKrOsb5w7d+5AqVTyLQwTplAocP36dY6zCeMYmz6OcfXAcTYttWrVgru7e5nbmZGesxekpKRgyZIl+PPPP2FmZgbgxSxfnTp1wrp167R2ojJWubm5sLOzQ05OjsawY48ePYKTk5NO7WgZY4wxxpjuxGIxMjMzUbdu3VK30zuRVcnIyMDly5dRWFiIJk2awNvbuzyHeaOVlsgCwN69e1GvXj2+8jNhqiv8t99+m+NsojjGpo9jXD1wnE2LrjWyOjcteJWHhweePn2Kli1bmlQtrD7q168Pb29v/sCYMIVCAZFIxHE2YRxj08cxrh44ztWTzqMWaDN69GhkZWUZqiyMMcYYY4zprEKJbDlbJejlk08+QWBgICIiIiCTydTWHTt2DA0aNEBQUBC6d+8OAPj7778RFBSEoKAgtGrVCu+++y4AIDo6Gn5+fggKCtI6EgJjjDHGGDMu5W5a8DqkpaXh7t27OH78OOLi4vDDDz9gxIgRatuEhIRg+fLlwv+NGjUSJleYP38+GjVqJKzbvHkzWrRo8VrKzhhjjDHGKleFamQXLFggTAdbGU6dOoVevXoBAPr06YOTJ09qbLNr1y4EBgbiiy++0FiXlJSEwYMHAwDMzMwwevRo9OzZE2lpaZVWZsYYY4wx9nroXSNbWFgIIoKVlRUGDhyIu3fvYseOHWjcuDECAgIMWrjs7GxhSlw7Ozs8efJEbX3btm1x7do1AMCgQYMQEBAAX19fAMDly5fh6uoKOzs7AMDy5cthb2+Pq1evIioqCqdPn9Y4X3FxMYqLi4X/8/PzyyyjQqEo35NjRkEVX46z6eIYmz6OcfXAcTYtunbY0zuR/fDDD9GzZ0+MGDECubm5CA4Ohrm5ObKzszF9+nSEhYXpXdjMzEyEhoZqLO/Vqxdyc3MBADk5OahTp47aemtra+HvgQMHIi0tTUhkd+7cieHDhwvrVTXHXl5eMDMzg0Kh0HiREhMTsXr1auF/XT4M6enpZW7DjB/H2fRxjE0fx7h64DibBlU+Vxa9E9lLly5hxowZAIDk5GTY29tjz549SE5ORkJCQrkSWWdnZ6Fd68suXLiAlStXIjIyEsnJyejUqZPa+tzcXGF81xMnTuCDDz4Q1iUlJeHo0aMa2z548ADFxcVaM/3Y2FiMHDlSbR9XV9dSy96yZUse5sOEKRQKpKenc5xNGMfY9HGMqweOc/VUrqYFNWvWBPAieezVq5cwbtu9e/cMWjhvb284OTkhMDAQbm5umDJlCoAXCWdiYiJ27NiB9evXw9zcHJ06dULnzp0BAFeuXFFrVgAA4eHhePLkCRQKhVrnsJdJpVK1MXF1mbVLLBbzB6Ya4DibPo6x6eMYVw8c5+pF70TWzc0Nhw8fRs+ePXHixAlER0cDAB4/fqx2q99QtA2VlZiYCAAYNWoURo0apbG+adOmSEpKUlv26v+MMcYYY8y46T1qwbhx4/Dvf/8b3bp1Q+vWrdGmTRsAwMmTJ9G0aVODF5AxxhhjjDFt9K6R7dOnD3x9ffHw4UN4eXkJyzt06IAePXoYtHCMMcYYY4yVpFwTIjg4OMDBwUFtWatWrQxSIMYYY4wxxnRRoQkRGGOMMcYYqypmRERVXYg3VW5uLuzs7JCTkyMM88UYY4wxxt4MnMiWgoiQl5cHGxsbmJmZVXVxGGOMMcbYSziRZYwxxhhjRonbyDLGGGOMMaPEiSxjjDHGGDNKnMgyxhhjjDGjVK5xZNn/dwRjjDHGGGOGp0tne05ky+nx48cak0IwxhhjjDHD0GX4U05ky8nCwgIeHh7473//C2tr66ouDqsk+fn56Ny5M8fZhHGMTR/HuHrgOJseGxubMrfhRLaczMzMIBaLYWtryx8YEyYSiTjOJo5jbPo4xtUDx7l64s5ejDHGGGPMKHEiyxhjjDHGjBInsuUklUoxfvx4SKXSqi4Kq0QcZ9PHMTZ9HOPqgeNcPfEUtYwxxhhjzChxjSxjjDHGGDNKnMgyxhhjjDGjxIksY4wxxhgzSpzIltOyZcsQFhaGqVOnQiaTVXVxmIFcvHgRISEheO+99zBp0iTIZDLs378foaGhiIqKQmZmZlUXkRnIzz//jPbt2wMAx9hEnT59GlFRUYiIiMChQ4eQkpKC0NBQjBgxAteuXavq4rEKUiqVmD59OsLCwjBixAhkZGRwjKshnhChHK5evYqsrCx88803WLduHZKTkzFgwICqLhYzAGdnZ3z11VewtLTEihUrcOTIEWzZsgXbtm1Deno61q5diwULFlR1MVkFKRQKHDhwAC4uLpDL5RxjE1RYWIjNmzdjw4YNQi/28PBwJCYmoqCgAPPmzcOGDRuquJSsIq5cuYLi4mJ88803SElJwZYtW/D3339zjKsZrpEth9TUVAQEBAAAAgMDkZqaWsUlYobi6OgIS0tLAIBEIsHff/8Nd3d3SKVS+Pr68hW+ifj555/Rp08fmJmZ4datWxxjE3T+/HlYWFhg7NixGDduHB48eACRSAQ7OzvUq1cPOTk5VV1EVkHOzs4gIhARcnJyYGVlxTGuhjiRLYfc3Fxh+jsbGxv+sJigu3fv4uTJk/D19VWb6lChUFRhqZghKBQK7N+/H/369QMA5OTkcIxN0OPHj3H79m2sW7cOwcHBWLVqlVqcxWIxiouLq7CErKJq164Nc3Nz9OnTB4sWLUJ0dDTHuBripgXlYGNjg/z8fABAXl4e7OzsqrhEzJDy8/Mxbdo0xMfHQ6lUCrEGXnwxMuOWlJSEvn37QiR6cR1va2vLMTZBNjY28PHxgVQqRYcOHZCQkICaNWsK6xUKBQ+cb+ROnDgBc3NzJCcnIz09HUuWLFH7LHOMqweukS0HHx8fnDp1CsCLD5KPj08Vl4gZilwux8SJEzF+/Hi4u7ujYcOG+N///ofi4mKkpqbC09OzqovIKujGjRvYu3cv3n//fdy6dQvbtm3jGJugli1bIiMjA0SEK1euoHHjxlAoFMjNzcX9+/e5AsIEEBFq1aoF4EXtbEFBAce4GuKZvcpp6dKlSEtLg4uLC+Lj4/mqz0Ts2bMH8fHxePvttwEAI0aMAABs3boVUqkUS5cuhYuLS1UWkRnQkCFDsHv3buzbt49jbIK2b9+Offv2wczMDIsXL0ZWVhZWrFgBAJg/fz68vLyquISsIuRyOaZMmYJHjx6huLgY06dPh0Kh4BhXM5zIMsYYY4wxo8RNCxhjjDHGmFHiRJYxxhhjjBklTmQZY4wxxphR4kSWMcYYY4wZJU5kGWOMMcaYUeJEljHGGGOMGSVOZBljjDHGmFHiRJYxxhhjjBklTmQZY4wxxphR4kSWMcYYY4wZJU5kGWOMMcaYUfo/HZVwA+MY3aQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 700x300 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# only plot system variables and observed context variables\n",
    "selected_variables=process_vars+observed_temporal_context_nodes+observed_spatial_context_nodes\n",
    "\n",
    "for dataset in [0,1]:\n",
    "    tp.plot_timeseries(selected_dataset = dataset, \n",
    "                       dataframe = dataframe, \n",
    "                       figsize=(7, 3), \n",
    "                       selected_variables=selected_variables)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb5f58e0",
   "metadata": {},
   "source": [
    "### Run J-PCMCI$^+$\n",
    "We set ``tau_max = 2``. Heuristics for choosing this hyper parameter are described in the PCMCI tutorial. There, it is also described how to choose an appropriate conditional independence test based on density plots. We further choose ``pc_alpha = 0.01`` and leave all other hyperparameters as default settings.\n",
    "\n",
    "As for PCMCIplus, the most important initilization parameters of the `JPCMCIplus` class are the `dataframe`, and the conditional independence test (`cond_ind_test`).\n",
    "Additionally, `JPCMCIplus` also needs to know which of the variables are system variables, which are time-context or space-context variables, and the respective dummy nodes. This information is passed as a dictionary in the `node_classification` parameter.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa295b37181f4f98",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "###### 1. With a variant of ParCorrMult"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "10c6dd395ae2d302",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-04T07:29:31.954816074Z",
     "start_time": "2023-12-04T07:29:28.349917154Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##\n",
      "## J-PCMCI+ Step 1: Selecting lagged conditioning sets\n",
      "##\n",
      "\n",
      "##\n",
      "## Step 1: PC1 algorithm for selecting lagged conditions\n",
      "##\n",
      "\n",
      "Parameters:\n",
      "link_assumptions = {0: {(0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>', (5, 0): '-?>', (6, 0): '-?>'}, 1: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>', (5, 0): '-?>', (6, 0): '-?>'}, 2: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>', (5, 0): '-?>', (6, 0): '-?>'}, 3: {(3, -1): '-?>', (3, -2): '-?>', (5, 0): 'o?o', (6, 0): 'o?o'}, 4: {(5, 0): 'o?o', (6, 0): 'o?o'}, 5: {}, 6: {}}\n",
      "independence test = par_corr_mult\n",
      "tau_min = 1\n",
      "tau_max = 2\n",
      "pc_alpha = [0.01]\n",
      "max_conds_dim = None\n",
      "max_combinations = 1\n",
      "\n",
      "\n",
      "## Resulting lagged parent (super)sets:\n",
      "\n",
      "    Variable $X_0$ has 2 link(s):\n",
      "        (t-$C_4$ -1): max_pval = 0.00000, |min_val| =  0.616\n",
      "        ($X_0$ -1): max_pval = 0.00000, |min_val| =  0.298\n",
      "\n",
      "    Variable $X_1$ has 1 link(s):\n",
      "        ($X_1$ -1): max_pval = 0.00000, |min_val| =  0.382\n",
      "\n",
      "    Variable $X_2$ has 5 link(s):\n",
      "        ($X_2$ -1): max_pval = 0.00000, |min_val| =  0.438\n",
      "        ($X_1$ -2): max_pval = 0.00000, |min_val| =  0.392\n",
      "        (t-$C_4$ -1): max_pval = 0.00000, |min_val| =  0.352\n",
      "        ($X_2$ -2): max_pval = 0.00000, |min_val| =  0.148\n",
      "        (t-$C_4$ -2): max_pval = 0.00300, |min_val| =  0.043\n",
      "\n",
      "    Variable t-$C_4$ has 2 link(s):\n",
      "        (t-$C_4$ -2): max_pval = 0.00000, |min_val| =  0.084\n",
      "        (t-$C_4$ -1): max_pval = 0.00000, |min_val| =  0.077\n",
      "\n",
      "    Variable s-$C_5$ has 0 link(s):\n",
      "\n",
      "    Variable t-dummy has 0 link(s):\n",
      "\n",
      "    Variable s-dummy has 0 link(s):\n",
      "\n",
      "##\n",
      "## J-PCMCI+ Step 2: Discovering context-system links\n",
      "##\n",
      "\n",
      "With link_assumptions = {0: {(0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>'}, 1: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>'}, 2: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>'}, 3: {(3, -1): '-?>', (3, -2): '-?>', (0, 0): '<?-', (1, 0): '<?-', (2, 0): '<?-'}, 4: {(0, 0): '<?-', (1, 0): '<?-', (2, 0): '<?-'}, 5: {}, 6: {}}\n",
      "\n",
      "## Significant links at alpha = 0.01:\n",
      "\n",
      "    Variable $X_0$ has 4 link(s):\n",
      "        (t-$C_4$ -1): pval = 0.00000 | val =  0.606\n",
      "        ($X_0$ -1): pval = 0.00000 | val =  0.000\n",
      "        ($X_1$  0): pval = 0.00000 | val =  0.000\n",
      "        ($X_2$  0): pval = 0.00000 | val =  0.000\n",
      "\n",
      "    Variable $X_1$ has 3 link(s):\n",
      "        ($X_0$  0): pval = 0.00000 | val =  0.000\n",
      "        ($X_1$ -1): pval = 0.00000 | val =  0.000\n",
      "        ($X_2$  0): pval = 0.00000 | val =  0.000\n",
      "\n",
      "    Variable $X_2$ has 7 link(s):\n",
      "        (s-$C_5$  0): pval = 0.00000 | val =  0.402\n",
      "        (t-$C_4$ -1): pval = 0.00000 | val =  0.351\n",
      "        ($X_0$  0): pval = 0.00000 | val =  0.000\n",
      "        ($X_1$  0): pval = 0.00000 | val =  0.000\n",
      "        ($X_1$ -2): pval = 0.00000 | val =  0.000\n",
      "        ($X_2$ -1): pval = 0.00000 | val =  0.000\n",
      "        ($X_2$ -2): pval = 0.00000 | val =  0.000\n",
      "\n",
      "    Variable t-$C_4$ has 2 link(s):\n",
      "        (t-$C_4$ -2): pval = 0.00000 | val =  0.080\n",
      "        (t-$C_4$ -1): pval = 0.00000 | val =  0.070\n",
      "\n",
      "    Variable s-$C_5$ has 1 link(s):\n",
      "        ($X_2$  0): pval = 0.00000 | val =  0.402\n",
      "\n",
      "    Variable t-dummy has 0 link(s):\n",
      "\n",
      "    Variable s-dummy has 0 link(s):\n",
      "\n",
      "##\n",
      "## J-PCMCI+ Step 3: Discovering dummy-system links\n",
      "##\n",
      "\n",
      "With link_assumptions = {0: {(0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (5, 0): '-?>', (6, 0): '-?>', (3, -1): '-->'}, 1: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (5, 0): '-?>', (6, 0): '-?>'}, 2: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, -1): '-?>', (2, -2): '-?>', (5, 0): '-?>', (6, 0): '-?>', (4, 0): '-->', (3, -1): '-->'}, 3: {(3, -1): '-->', (3, -2): '-->'}, 4: {(2, 0): '<--'}, 5: {(0, 0): '<?-', (1, 0): '<?-', (2, 0): '<?-'}, 6: {(0, 0): '<?-', (1, 0): '<?-', (2, 0): '<?-'}}\n",
      "\n",
      "## Significant links at alpha = 0.01:\n",
      "\n",
      "    Variable $X_0$ has 5 link(s):\n",
      "        (t-$C_4$ -1): pval = 0.00000 | val =  0.606\n",
      "        (t-dummy  0): pval = 0.00000 | val =  0.119\n",
      "        ($X_0$ -1): pval = 0.00000 | val =  0.000\n",
      "        ($X_1$  0): pval = 0.00000 | val =  0.000\n",
      "        ($X_2$  0): pval = 0.00000 | val =  0.000\n",
      "\n",
      "    Variable $X_1$ has 4 link(s):\n",
      "        (t-dummy  0): pval = 0.00001 | val =  0.078\n",
      "        ($X_0$  0): pval = 0.00000 | val =  0.000\n",
      "        ($X_1$ -1): pval = 0.00000 | val =  0.000\n",
      "        ($X_2$  0): pval = 0.00000 | val =  0.000\n",
      "\n",
      "    Variable $X_2$ has 7 link(s):\n",
      "        (s-$C_5$  0): pval = 0.00000 | val =  0.402\n",
      "        (t-$C_4$ -1): pval = 0.00000 | val =  0.351\n",
      "        ($X_0$  0): pval = 0.00000 | val =  0.000\n",
      "        ($X_1$  0): pval = 0.00000 | val =  0.000\n",
      "        ($X_1$ -2): pval = 0.00000 | val =  0.000\n",
      "        ($X_2$ -1): pval = 0.00000 | val =  0.000\n",
      "        ($X_2$ -2): pval = 0.00000 | val =  0.000\n",
      "\n",
      "    Variable t-$C_4$ has 2 link(s):\n",
      "        (t-$C_4$ -1): pval = 0.00000 | val =  0.000\n",
      "        (t-$C_4$ -2): pval = 0.00000 | val =  0.000\n",
      "\n",
      "    Variable s-$C_5$ has 0 link(s):\n",
      "\n",
      "    Variable t-dummy has 2 link(s):\n",
      "        ($X_0$  0): pval = 0.00000 | val =  0.119\n",
      "        ($X_1$  0): pval = 0.00001 | val =  0.078\n",
      "\n",
      "    Variable s-dummy has 0 link(s):\n",
      "\n",
      "##\n",
      "## J-PCMCI+ Step 4: Discovering system-system links \n",
      "##\n",
      "\n",
      "With link_assumptions = {0: {(0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (3, -1): '-->', (5, 0): '-->'}, 1: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (5, 0): '-->'}, 2: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, -1): '-?>', (2, -2): '-?>', (4, 0): '-->', (3, -1): '-->'}, 3: {}, 4: {(2, 0): '<--'}, 5: {(0, 0): '<--', (1, 0): '<--'}, 6: {}}\n",
      "\n",
      "## Significant links at alpha = 0.01:\n",
      "\n",
      "    Variable $X_0$ has 3 link(s):\n",
      "        (t-$C_4$ -1): pval = 0.00000 | val =  0.606\n",
      "        ($X_0$ -1): pval = 0.00000 | val =  0.296\n",
      "        (t-dummy  0): pval = 0.00000 | val =  0.119\n",
      "\n",
      "    Variable $X_1$ has 2 link(s):\n",
      "        ($X_1$ -1): pval = 0.00000 | val =  0.382\n",
      "        (t-dummy  0): pval = 0.00001 | val =  0.078\n",
      "\n",
      "    Variable $X_2$ has 4 link(s):\n",
      "        ($X_1$ -2): pval = 0.00000 | val = -0.443\n",
      "        (s-$C_5$  0): pval = 0.00000 | val =  0.402\n",
      "        (t-$C_4$ -1): pval = 0.00000 | val =  0.351\n",
      "        ($X_2$ -1): pval = 0.00000 | val =  0.291\n",
      "\n",
      "    Variable t-$C_4$ has 0 link(s):\n",
      "\n",
      "    Variable s-$C_5$ has 0 link(s):\n",
      "\n",
      "    Variable t-dummy has 0 link(s):\n",
      "\n",
      "    Variable s-dummy has 0 link(s):\n"
     ]
    }
   ],
   "source": [
    "# Categorize all the nodes into system, context, or dummy\n",
    "node_classification_jpcmci = {i: node_classification[var] for i, var in enumerate(observed_indices)}\n",
    "node_classification_jpcmci.update({time_dummy_index : \"time_dummy\", space_dummy_index : \"space_dummy\"})\n",
    "\n",
    "# Create a J-PCMCI+ object, passing the dataframe and (conditional)\n",
    "# independence test objects, as well as the observed temporal and spatial context nodes \n",
    "# and the indices of the dummies.\n",
    "jpcmciplus = JPCMCIplus(dataframe=dataframe,\n",
    "                          cond_ind_test=ParCorrMult(significance='analytic'), \n",
    "                          node_classification=node_classification_jpcmci,\n",
    "                          verbosity=1,)\n",
    "\n",
    "# Define the analysis parameters.\n",
    "tau_max = 2\n",
    "pc_alpha = 0.01\n",
    "\n",
    "# Run J-PCMCI+\n",
    "results = jpcmciplus.run_jpcmciplus(tau_min=0, \n",
    "                              tau_max=tau_max, \n",
    "                              pc_alpha=pc_alpha)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3b0617d7d126fae",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "###### 2. With RegressionCI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f9082826",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-04T07:29:40.880335164Z",
     "start_time": "2023-12-04T07:29:34.520920809Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##\n",
      "## J-PCMCI+ Step 1: Selecting lagged conditioning sets\n",
      "##\n",
      "\n",
      "##\n",
      "## Step 1: PC1 algorithm for selecting lagged conditions\n",
      "##\n",
      "\n",
      "Parameters:\n",
      "link_assumptions = {0: {(0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>', (5, 0): '-?>', (6, 0): '-?>'}, 1: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>', (5, 0): '-?>', (6, 0): '-?>'}, 2: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>', (5, 0): '-?>', (6, 0): '-?>'}, 3: {(3, -1): '-?>', (3, -2): '-?>', (5, 0): 'o?o', (6, 0): 'o?o'}, 4: {(5, 0): 'o?o', (6, 0): 'o?o'}, 5: {}, 6: {}}\n",
      "independence test = regression_ci\n",
      "tau_min = 1\n",
      "tau_max = 2\n",
      "pc_alpha = [0.01]\n",
      "max_conds_dim = None\n",
      "max_combinations = 1\n",
      "\n",
      "\n",
      "\n",
      "## Variable $X_0$\n",
      "\n",
      "Iterating through pc_alpha = [0.01]:\n",
      "\n",
      "# pc_alpha = 0.01 (1/1):\n",
      "\n",
      "Testing condition sets of dimension 0:\n",
      "\n",
      "    Link ($X_0$ -1) -?> $X_0$ (1/8):\n",
      "    Subset 0: () gives pval = 0.00000 / val =  604.287\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link ($X_0$ -2) -?> $X_0$ (2/8):\n",
      "    Subset 0: () gives pval = 0.00000 / val =  156.746\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link ($X_1$ -1) -?> $X_0$ (3/8):\n",
      "    Subset 0: () gives pval = 0.16872 / val =  1.894\n",
      "    Non-significance detected.\n",
      "\n",
      "    Link ($X_1$ -2) -?> $X_0$ (4/8):\n",
      "    Subset 0: () gives pval = 0.54624 / val =  0.364\n",
      "    Non-significance detected.\n",
      "\n",
      "    Link ($X_2$ -1) -?> $X_0$ (5/8):\n",
      "    Subset 0: () gives pval = 0.00000 / val =  31.687\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link ($X_2$ -2) -?> $X_0$ (6/8):\n",
      "    Subset 0: () gives pval = 0.00142 / val =  10.174\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link (t-$C_4$ -1) -?> $X_0$ (7/8):\n",
      "    Subset 0: () gives pval = 0.00000 / val =  2286.931\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link (t-$C_4$ -2) -?> $X_0$ (8/8):\n",
      "    Subset 0: () gives pval = 0.00000 / val =  260.921\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Sorting parents in decreasing order with \n",
      "    weight(i-tau->j) = min_{iterations} |val_{ij}(tau)| \n",
      "\n",
      "Updating parents:\n",
      "\n",
      "    Variable $X_0$ has 6 link(s):\n",
      "        (t-$C_4$ -1): max_pval = 0.00000, |min_val| =  2286.931\n",
      "        ($X_0$ -1): max_pval = 0.00000, |min_val| =  604.287\n",
      "        (t-$C_4$ -2): max_pval = 0.00000, |min_val| =  260.921\n",
      "        ($X_0$ -2): max_pval = 0.00000, |min_val| =  156.746\n",
      "        ($X_2$ -1): max_pval = 0.00000, |min_val| =  31.687\n",
      "        ($X_2$ -2): max_pval = 0.00142, |min_val| =  10.174\n",
      "\n",
      "Testing condition sets of dimension 1:\n",
      "\n",
      "    Link (t-$C_4$ -1) -?> $X_0$ (1/6):\n",
      "    Subset 0: ($X_0$ -1)  gives pval = 0.00000 / val =  2418.407\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "    Link ($X_0$ -1) -?> $X_0$ (2/6):\n",
      "    Subset 0: (t-$C_4$ -1)  gives pval = 0.00000 / val =  735.763\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "    Link (t-$C_4$ -2) -?> $X_0$ (3/6):\n",
      "    Subset 0: (t-$C_4$ -1)  gives pval = 0.00000 / val =  288.755\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "    Link ($X_0$ -2) -?> $X_0$ (4/6):\n",
      "    Subset 0: (t-$C_4$ -1)  gives pval = 0.00000 / val =  124.947\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "    Link ($X_2$ -1) -?> $X_0$ (5/6):\n",
      "    Subset 0: (t-$C_4$ -1)  gives pval = 0.00000 / val =  22.137\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "    Link ($X_2$ -2) -?> $X_0$ (6/6):\n",
      "    Subset 0: (t-$C_4$ -1)  gives pval = 0.22814 / val =  1.452\n",
      "    Non-significance detected.\n",
      "\n",
      "    Sorting parents in decreasing order with \n",
      "    weight(i-tau->j) = min_{iterations} |val_{ij}(tau)| \n",
      "\n",
      "Updating parents:\n",
      "\n",
      "    Variable $X_0$ has 5 link(s):\n",
      "        (t-$C_4$ -1): max_pval = 0.00000, |min_val| =  2286.931\n",
      "        ($X_0$ -1): max_pval = 0.00000, |min_val| =  604.287\n",
      "        (t-$C_4$ -2): max_pval = 0.00000, |min_val| =  260.921\n",
      "        ($X_0$ -2): max_pval = 0.00000, |min_val| =  124.947\n",
      "        ($X_2$ -1): max_pval = 0.00000, |min_val| =  22.137\n",
      "\n",
      "Testing condition sets of dimension 2:\n",
      "\n",
      "    Link (t-$C_4$ -1) -?> $X_0$ (1/5):\n",
      "    Subset 0: ($X_0$ -1) (t-$C_4$ -2)  gives pval = 0.00000 / val =  2416.490\n",
      "    Still subsets of dimension 2 left, but q_max = 1 reached.\n",
      "\n",
      "    Link ($X_0$ -1) -?> $X_0$ (2/5):\n",
      "    Subset 0: (t-$C_4$ -1) (t-$C_4$ -2)  gives pval = 0.00000 / val =  447.834\n",
      "    Still subsets of dimension 2 left, but q_max = 1 reached.\n",
      "\n",
      "    Link (t-$C_4$ -2) -?> $X_0$ (3/5):\n",
      "    Subset 0: (t-$C_4$ -1) ($X_0$ -1)  gives pval = 0.36355 / val =  0.826\n",
      "    Non-significance detected.\n",
      "\n",
      "    Link ($X_0$ -2) -?> $X_0$ (4/5):\n",
      "    Subset 0: (t-$C_4$ -1) ($X_0$ -1)  gives pval = 0.01039 / val =  6.567\n",
      "    Non-significance detected.\n",
      "\n",
      "    Link ($X_2$ -1) -?> $X_0$ (5/5):\n",
      "    Subset 0: (t-$C_4$ -1) ($X_0$ -1)  gives pval = 0.15644 / val =  2.008\n",
      "    Non-significance detected.\n",
      "\n",
      "    Sorting parents in decreasing order with \n",
      "    weight(i-tau->j) = min_{iterations} |val_{ij}(tau)| \n",
      "\n",
      "Updating parents:\n",
      "\n",
      "    Variable $X_0$ has 2 link(s):\n",
      "        (t-$C_4$ -1): max_pval = 0.00000, |min_val| =  2286.931\n",
      "        ($X_0$ -1): max_pval = 0.00000, |min_val| =  447.834\n",
      "\n",
      "Algorithm converged for variable $X_0$\n",
      "\n",
      "## Variable $X_1$\n",
      "\n",
      "Iterating through pc_alpha = [0.01]:\n",
      "\n",
      "# pc_alpha = 0.01 (1/1):\n",
      "\n",
      "Testing condition sets of dimension 0:\n",
      "\n",
      "    Link ($X_0$ -1) -?> $X_1$ (1/8):\n",
      "    Subset 0: () gives pval = 0.00069 / val =  11.512\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link ($X_0$ -2) -?> $X_1$ (2/8):\n",
      "    Subset 0: () gives pval = 0.01805 / val =  5.591\n",
      "    Non-significance detected.\n",
      "\n",
      "    Link ($X_1$ -1) -?> $X_1$ (3/8):\n",
      "    Subset 0: () gives pval = 0.00000 / val =  898.311\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link ($X_1$ -2) -?> $X_1$ (4/8):\n",
      "    Subset 0: () gives pval = 0.00000 / val =  142.716\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link ($X_2$ -1) -?> $X_1$ (5/8):\n",
      "    Subset 0: () gives pval = 0.00050 / val =  12.133\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link ($X_2$ -2) -?> $X_1$ (6/8):\n",
      "    Subset 0: () gives pval = 0.00286 / val =  8.898\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link (t-$C_4$ -1) -?> $X_1$ (7/8):\n",
      "    Subset 0: () gives pval = 0.21959 / val =  1.507\n",
      "    Non-significance detected.\n",
      "\n",
      "    Link (t-$C_4$ -2) -?> $X_1$ (8/8):\n",
      "    Subset 0: () gives pval = 0.37218 / val =  0.796\n",
      "    Non-significance detected.\n",
      "\n",
      "    Sorting parents in decreasing order with \n",
      "    weight(i-tau->j) = min_{iterations} |val_{ij}(tau)| \n",
      "\n",
      "Updating parents:\n",
      "\n",
      "    Variable $X_1$ has 5 link(s):\n",
      "        ($X_1$ -1): max_pval = 0.00000, |min_val| =  898.311\n",
      "        ($X_1$ -2): max_pval = 0.00000, |min_val| =  142.716\n",
      "        ($X_2$ -1): max_pval = 0.00050, |min_val| =  12.133\n",
      "        ($X_0$ -1): max_pval = 0.00069, |min_val| =  11.512\n",
      "        ($X_2$ -2): max_pval = 0.00286, |min_val| =  8.898\n",
      "\n",
      "Testing condition sets of dimension 1:\n",
      "\n",
      "    Link ($X_1$ -1) -?> $X_1$ (1/5):\n",
      "    Subset 0: ($X_1$ -2)  gives pval = 0.00000 / val =  755.629\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "    Link ($X_1$ -2) -?> $X_1$ (2/5):\n",
      "    Subset 0: ($X_1$ -1)  gives pval = 0.85376 / val =  0.034\n",
      "    Non-significance detected.\n",
      "\n",
      "    Link ($X_2$ -1) -?> $X_1$ (3/5):\n",
      "    Subset 0: ($X_1$ -1)  gives pval = 0.41096 / val =  0.676\n",
      "    Non-significance detected.\n",
      "\n",
      "    Link ($X_0$ -1) -?> $X_1$ (4/5):\n",
      "    Subset 0: ($X_1$ -1)  gives pval = 0.53034 / val =  0.394\n",
      "    Non-significance detected.\n",
      "\n",
      "    Link ($X_2$ -2) -?> $X_1$ (5/5):\n",
      "    Subset 0: ($X_1$ -1)  gives pval = 0.10480 / val =  2.631\n",
      "    Non-significance detected.\n",
      "\n",
      "    Sorting parents in decreasing order with \n",
      "    weight(i-tau->j) = min_{iterations} |val_{ij}(tau)| \n",
      "\n",
      "Updating parents:\n",
      "\n",
      "    Variable $X_1$ has 1 link(s):\n",
      "        ($X_1$ -1): max_pval = 0.00000, |min_val| =  755.629\n",
      "\n",
      "Algorithm converged for variable $X_1$\n",
      "\n",
      "## Variable $X_2$\n",
      "\n",
      "Iterating through pc_alpha = [0.01]:\n",
      "\n",
      "# pc_alpha = 0.01 (1/1):\n",
      "\n",
      "Testing condition sets of dimension 0:\n",
      "\n",
      "    Link ($X_0$ -1) -?> $X_2$ (1/8):\n",
      "    Subset 0: () gives pval = 0.00000 / val =  32.682\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link ($X_0$ -2) -?> $X_2$ (2/8):\n",
      "    Subset 0: () gives pval = 0.08587 / val =  2.950\n",
      "    Non-significance detected.\n",
      "\n",
      "    Link ($X_1$ -1) -?> $X_2$ (3/8):\n",
      "    Subset 0: () gives pval = 0.00000 / val =  146.541\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link ($X_1$ -2) -?> $X_2$ (4/8):\n",
      "    Subset 0: () gives pval = 0.00000 / val =  910.608\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link ($X_2$ -1) -?> $X_2$ (5/8):\n",
      "    Subset 0: () gives pval = 0.00000 / val =  1865.380\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link ($X_2$ -2) -?> $X_2$ (6/8):\n",
      "    Subset 0: () gives pval = 0.00000 / val =  950.004\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link (t-$C_4$ -1) -?> $X_2$ (7/8):\n",
      "    Subset 0: () gives pval = 0.00000 / val =  633.770\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link (t-$C_4$ -2) -?> $X_2$ (8/8):\n",
      "    Subset 0: () gives pval = 0.00000 / val =  74.313\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Sorting parents in decreasing order with \n",
      "    weight(i-tau->j) = min_{iterations} |val_{ij}(tau)| \n",
      "\n",
      "Updating parents:\n",
      "\n",
      "    Variable $X_2$ has 7 link(s):\n",
      "        ($X_2$ -1): max_pval = 0.00000, |min_val| =  1865.380\n",
      "        ($X_2$ -2): max_pval = 0.00000, |min_val| =  950.004\n",
      "        ($X_1$ -2): max_pval = 0.00000, |min_val| =  910.608\n",
      "        (t-$C_4$ -1): max_pval = 0.00000, |min_val| =  633.770\n",
      "        ($X_1$ -1): max_pval = 0.00000, |min_val| =  146.541\n",
      "        (t-$C_4$ -2): max_pval = 0.00000, |min_val| =  74.313\n",
      "        ($X_0$ -1): max_pval = 0.00000, |min_val| =  32.682\n",
      "\n",
      "Testing condition sets of dimension 1:\n",
      "\n",
      "    Link ($X_2$ -1) -?> $X_2$ (1/7):\n",
      "    Subset 0: ($X_2$ -2)  gives pval = 0.00000 / val =  1023.086\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "    Link ($X_2$ -2) -?> $X_2$ (2/7):\n",
      "    Subset 0: ($X_2$ -1)  gives pval = 0.00000 / val =  107.709\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "    Link ($X_1$ -2) -?> $X_2$ (3/7):\n",
      "    Subset 0: ($X_2$ -1)  gives pval = 0.00000 / val =  800.010\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "    Link (t-$C_4$ -1) -?> $X_2$ (4/7):\n",
      "    Subset 0: ($X_2$ -1)  gives pval = 0.00000 / val =  821.000\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "    Link ($X_1$ -1) -?> $X_2$ (5/7):\n",
      "    Subset 0: ($X_2$ -1)  gives pval = 0.00000 / val =  102.549\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "    Link (t-$C_4$ -2) -?> $X_2$ (6/7):\n",
      "    Subset 0: ($X_2$ -1)  gives pval = 0.00000 / val =  45.895\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "    Link ($X_0$ -1) -?> $X_2$ (7/7):\n",
      "    Subset 0: ($X_2$ -1)  gives pval = 0.00002 / val =  17.957\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "    Sorting parents in decreasing order with \n",
      "    weight(i-tau->j) = min_{iterations} |val_{ij}(tau)| \n",
      "\n",
      "Updating parents:\n",
      "\n",
      "    Variable $X_2$ has 7 link(s):\n",
      "        ($X_2$ -1): max_pval = 0.00000, |min_val| =  1023.086\n",
      "        ($X_1$ -2): max_pval = 0.00000, |min_val| =  800.010\n",
      "        (t-$C_4$ -1): max_pval = 0.00000, |min_val| =  633.770\n",
      "        ($X_2$ -2): max_pval = 0.00000, |min_val| =  107.709\n",
      "        ($X_1$ -1): max_pval = 0.00000, |min_val| =  102.549\n",
      "        (t-$C_4$ -2): max_pval = 0.00000, |min_val| =  45.895\n",
      "        ($X_0$ -1): max_pval = 0.00002, |min_val| =  17.957\n",
      "\n",
      "Testing condition sets of dimension 2:\n",
      "\n",
      "    Link ($X_2$ -1) -?> $X_2$ (1/7):\n",
      "    Subset 0: ($X_1$ -2) (t-$C_4$ -1)  gives pval = 0.00000 / val =  1978.521\n",
      "    Still subsets of dimension 2 left, but q_max = 1 reached.\n",
      "\n",
      "    Link ($X_1$ -2) -?> $X_2$ (2/7):\n",
      "    Subset 0: ($X_2$ -1) (t-$C_4$ -1)  gives pval = 0.00000 / val =  961.723\n",
      "    Still subsets of dimension 2 left, but q_max = 1 reached.\n",
      "\n",
      "    Link (t-$C_4$ -1) -?> $X_2$ (3/7):\n",
      "    Subset 0: ($X_2$ -1) ($X_1$ -2)  gives pval = 0.00000 / val =  982.713\n",
      "    Still subsets of dimension 2 left, but q_max = 1 reached.\n",
      "\n",
      "    Link ($X_2$ -2) -?> $X_2$ (4/7):\n",
      "    Subset 0: ($X_2$ -1) ($X_1$ -2)  gives pval = 0.00000 / val =  128.879\n",
      "    Still subsets of dimension 2 left, but q_max = 1 reached.\n",
      "\n",
      "    Link ($X_1$ -1) -?> $X_2$ (5/7):\n",
      "    Subset 0: ($X_2$ -1) ($X_1$ -2)  gives pval = 0.34724 / val =  0.884\n",
      "    Non-significance detected.\n",
      "\n",
      "    Link (t-$C_4$ -2) -?> $X_2$ (6/7):\n",
      "    Subset 0: ($X_2$ -1) ($X_1$ -2)  gives pval = 0.00000 / val =  24.964\n",
      "    Still subsets of dimension 2 left, but q_max = 1 reached.\n",
      "\n",
      "    Link ($X_0$ -1) -?> $X_2$ (7/7):\n",
      "    Subset 0: ($X_2$ -1) ($X_1$ -2)  gives pval = 0.00501 / val =  7.874\n",
      "    Still subsets of dimension 2 left, but q_max = 1 reached.\n",
      "\n",
      "    Sorting parents in decreasing order with \n",
      "    weight(i-tau->j) = min_{iterations} |val_{ij}(tau)| \n",
      "\n",
      "Updating parents:\n",
      "\n",
      "    Variable $X_2$ has 6 link(s):\n",
      "        ($X_2$ -1): max_pval = 0.00000, |min_val| =  1023.086\n",
      "        ($X_1$ -2): max_pval = 0.00000, |min_val| =  800.010\n",
      "        (t-$C_4$ -1): max_pval = 0.00000, |min_val| =  633.770\n",
      "        ($X_2$ -2): max_pval = 0.00000, |min_val| =  107.709\n",
      "        (t-$C_4$ -2): max_pval = 0.00000, |min_val| =  24.964\n",
      "        ($X_0$ -1): max_pval = 0.00501, |min_val| =  7.874\n",
      "\n",
      "Testing condition sets of dimension 3:\n",
      "\n",
      "    Link ($X_2$ -1) -?> $X_2$ (1/6):\n",
      "    Subset 0: ($X_1$ -2) (t-$C_4$ -1) ($X_2$ -2)  gives pval = 0.00000 / val =  1089.545\n",
      "    Still subsets of dimension 3 left, but q_max = 1 reached.\n",
      "\n",
      "    Link ($X_1$ -2) -?> $X_2$ (2/6):\n",
      "    Subset 0: ($X_2$ -1) (t-$C_4$ -1) ($X_2$ -2)  gives pval = 0.00000 / val =  987.276\n",
      "    Still subsets of dimension 3 left, but q_max = 1 reached.\n",
      "\n",
      "    Link (t-$C_4$ -1) -?> $X_2$ (3/6):\n",
      "    Subset 0: ($X_2$ -1) ($X_1$ -2) ($X_2$ -2)  gives pval = 0.00000 / val =  985.989\n",
      "    Still subsets of dimension 3 left, but q_max = 1 reached.\n",
      "\n",
      "    Link ($X_2$ -2) -?> $X_2$ (4/6):\n",
      "    Subset 0: ($X_2$ -1) ($X_1$ -2) (t-$C_4$ -1)  gives pval = 0.00000 / val =  132.155\n",
      "    Still subsets of dimension 3 left, but q_max = 1 reached.\n",
      "\n",
      "    Link (t-$C_4$ -2) -?> $X_2$ (5/6):\n",
      "    Subset 0: ($X_2$ -1) ($X_1$ -2) (t-$C_4$ -1)  gives pval = 0.00000 / val =  53.359\n",
      "    Still subsets of dimension 3 left, but q_max = 1 reached.\n",
      "\n",
      "    Link ($X_0$ -1) -?> $X_2$ (6/6):\n",
      "    Subset 0: ($X_2$ -1) ($X_1$ -2) (t-$C_4$ -1)  gives pval = 0.00000 / val =  29.356\n",
      "    Still subsets of dimension 3 left, but q_max = 1 reached.\n",
      "\n",
      "    Sorting parents in decreasing order with \n",
      "    weight(i-tau->j) = min_{iterations} |val_{ij}(tau)| \n",
      "\n",
      "Updating parents:\n",
      "\n",
      "    Variable $X_2$ has 6 link(s):\n",
      "        ($X_2$ -1): max_pval = 0.00000, |min_val| =  1023.086\n",
      "        ($X_1$ -2): max_pval = 0.00000, |min_val| =  800.010\n",
      "        (t-$C_4$ -1): max_pval = 0.00000, |min_val| =  633.770\n",
      "        ($X_2$ -2): max_pval = 0.00000, |min_val| =  107.709\n",
      "        (t-$C_4$ -2): max_pval = 0.00000, |min_val| =  24.964\n",
      "        ($X_0$ -1): max_pval = 0.00501, |min_val| =  7.874\n",
      "\n",
      "Testing condition sets of dimension 4:\n",
      "\n",
      "    Link ($X_2$ -1) -?> $X_2$ (1/6):\n",
      "    Subset 0: ($X_1$ -2) (t-$C_4$ -1) ($X_2$ -2) (t-$C_4$ -2)  gives pval = 0.00000 / val =  1047.226\n",
      "    Still subsets of dimension 4 left, but q_max = 1 reached.\n",
      "\n",
      "    Link ($X_1$ -2) -?> $X_2$ (2/6):\n",
      "    Subset 0: ($X_2$ -1) (t-$C_4$ -1) ($X_2$ -2) (t-$C_4$ -2)  gives pval = 0.00000 / val =  963.139\n",
      "    Still subsets of dimension 4 left, but q_max = 1 reached.\n",
      "\n",
      "    Link (t-$C_4$ -1) -?> $X_2$ (3/6):\n",
      "    Subset 0: ($X_2$ -1) ($X_1$ -2) ($X_2$ -2) (t-$C_4$ -2)  gives pval = 0.00000 / val =  1004.685\n",
      "    Still subsets of dimension 4 left, but q_max = 1 reached.\n",
      "\n",
      "    Link ($X_2$ -2) -?> $X_2$ (4/6):\n",
      "    Subset 0: ($X_2$ -1) ($X_1$ -2) (t-$C_4$ -1) (t-$C_4$ -2)  gives pval = 0.00000 / val =  105.613\n",
      "    Still subsets of dimension 4 left, but q_max = 1 reached.\n",
      "\n",
      "    Link (t-$C_4$ -2) -?> $X_2$ (5/6):\n",
      "    Subset 0: ($X_2$ -1) ($X_1$ -2) (t-$C_4$ -1) ($X_2$ -2)  gives pval = 0.00000 / val =  26.816\n",
      "    Still subsets of dimension 4 left, but q_max = 1 reached.\n",
      "\n",
      "    Link ($X_0$ -1) -?> $X_2$ (6/6):\n",
      "    Subset 0: ($X_2$ -1) ($X_1$ -2) (t-$C_4$ -1) ($X_2$ -2)  gives pval = 0.00000 / val =  22.447\n",
      "    Still subsets of dimension 4 left, but q_max = 1 reached.\n",
      "\n",
      "    Sorting parents in decreasing order with \n",
      "    weight(i-tau->j) = min_{iterations} |val_{ij}(tau)| \n",
      "\n",
      "Updating parents:\n",
      "\n",
      "    Variable $X_2$ has 6 link(s):\n",
      "        ($X_2$ -1): max_pval = 0.00000, |min_val| =  1023.086\n",
      "        ($X_1$ -2): max_pval = 0.00000, |min_val| =  800.010\n",
      "        (t-$C_4$ -1): max_pval = 0.00000, |min_val| =  633.770\n",
      "        ($X_2$ -2): max_pval = 0.00000, |min_val| =  105.613\n",
      "        (t-$C_4$ -2): max_pval = 0.00000, |min_val| =  24.964\n",
      "        ($X_0$ -1): max_pval = 0.00501, |min_val| =  7.874\n",
      "\n",
      "Testing condition sets of dimension 5:\n",
      "\n",
      "    Link ($X_2$ -1) -?> $X_2$ (1/6):\n",
      "    Subset 0: ($X_1$ -2) (t-$C_4$ -1) ($X_2$ -2) (t-$C_4$ -2) ($X_0$ -1)  gives pval = 0.00000 / val =  1045.376\n",
      "    Still subsets of dimension 5 left, but q_max = 1 reached.\n",
      "\n",
      "    Link ($X_1$ -2) -?> $X_2$ (2/6):\n",
      "    Subset 0: ($X_2$ -1) (t-$C_4$ -1) ($X_2$ -2) (t-$C_4$ -2) ($X_0$ -1)  gives pval = 0.00000 / val =  961.894\n",
      "    Still subsets of dimension 5 left, but q_max = 1 reached.\n",
      "\n",
      "    Link (t-$C_4$ -1) -?> $X_2$ (3/6):\n",
      "    Subset 0: ($X_2$ -1) ($X_1$ -2) ($X_2$ -2) (t-$C_4$ -2) ($X_0$ -1)  gives pval = 0.00000 / val =  1008.829\n",
      "    Still subsets of dimension 5 left, but q_max = 1 reached.\n",
      "\n",
      "    Link ($X_2$ -2) -?> $X_2$ (4/6):\n",
      "    Subset 0: ($X_2$ -1) ($X_1$ -2) (t-$C_4$ -1) (t-$C_4$ -2) ($X_0$ -1)  gives pval = 0.00000 / val =  108.045\n",
      "    Still subsets of dimension 5 left, but q_max = 1 reached.\n",
      "\n",
      "    Link (t-$C_4$ -2) -?> $X_2$ (5/6):\n",
      "    Subset 0: ($X_2$ -1) ($X_1$ -2) (t-$C_4$ -1) ($X_2$ -2) ($X_0$ -1)  gives pval = 0.00297 / val =  8.823\n",
      "    Still subsets of dimension 5 left, but q_max = 1 reached.\n",
      "\n",
      "    Link ($X_0$ -1) -?> $X_2$ (6/6):\n",
      "    Subset 0: ($X_2$ -1) ($X_1$ -2) (t-$C_4$ -1) ($X_2$ -2) (t-$C_4$ -2)  gives pval = 0.03482 / val =  4.454\n",
      "    Non-significance detected.\n",
      "\n",
      "    Sorting parents in decreasing order with \n",
      "    weight(i-tau->j) = min_{iterations} |val_{ij}(tau)| \n",
      "\n",
      "Updating parents:\n",
      "\n",
      "    Variable $X_2$ has 5 link(s):\n",
      "        ($X_2$ -1): max_pval = 0.00000, |min_val| =  1023.086\n",
      "        ($X_1$ -2): max_pval = 0.00000, |min_val| =  800.010\n",
      "        (t-$C_4$ -1): max_pval = 0.00000, |min_val| =  633.770\n",
      "        ($X_2$ -2): max_pval = 0.00000, |min_val| =  105.613\n",
      "        (t-$C_4$ -2): max_pval = 0.00297, |min_val| =  8.823\n",
      "\n",
      "Algorithm converged for variable $X_2$\n",
      "\n",
      "## Variable t-$C_4$\n",
      "\n",
      "Iterating through pc_alpha = [0.01]:\n",
      "\n",
      "# pc_alpha = 0.01 (1/1):\n",
      "\n",
      "Testing condition sets of dimension 0:\n",
      "\n",
      "    Link (t-$C_4$ -1) -?> t-$C_4$ (1/2):\n",
      "    Subset 0: () gives pval = 0.00000 / val =  32.774\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link (t-$C_4$ -2) -?> t-$C_4$ (2/2):\n",
      "    Subset 0: () gives pval = 0.00000 / val =  37.921\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Sorting parents in decreasing order with \n",
      "    weight(i-tau->j) = min_{iterations} |val_{ij}(tau)| \n",
      "\n",
      "Updating parents:\n",
      "\n",
      "    Variable t-$C_4$ has 2 link(s):\n",
      "        (t-$C_4$ -2): max_pval = 0.00000, |min_val| =  37.921\n",
      "        (t-$C_4$ -1): max_pval = 0.00000, |min_val| =  32.774\n",
      "\n",
      "Testing condition sets of dimension 1:\n",
      "\n",
      "    Link (t-$C_4$ -2) -?> t-$C_4$ (1/2):\n",
      "    Subset 0: (t-$C_4$ -1)  gives pval = 0.00000 / val =  33.834\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "    Link (t-$C_4$ -1) -?> t-$C_4$ (2/2):\n",
      "    Subset 0: (t-$C_4$ -2)  gives pval = 0.00000 / val =  28.687\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "    Sorting parents in decreasing order with \n",
      "    weight(i-tau->j) = min_{iterations} |val_{ij}(tau)| \n",
      "\n",
      "Updating parents:\n",
      "\n",
      "    Variable t-$C_4$ has 2 link(s):\n",
      "        (t-$C_4$ -2): max_pval = 0.00000, |min_val| =  33.834\n",
      "        (t-$C_4$ -1): max_pval = 0.00000, |min_val| =  28.687\n",
      "\n",
      "Algorithm converged for variable t-$C_4$\n",
      "\n",
      "## Variable s-$C_5$\n",
      "\n",
      "Iterating through pc_alpha = [0.01]:\n",
      "\n",
      "# pc_alpha = 0.01 (1/1):\n",
      "\n",
      "Algorithm converged for variable s-$C_5$\n",
      "\n",
      "## Variable t-dummy\n",
      "\n",
      "Iterating through pc_alpha = [0.01]:\n",
      "\n",
      "# pc_alpha = 0.01 (1/1):\n",
      "\n",
      "Algorithm converged for variable t-dummy\n",
      "\n",
      "## Variable s-dummy\n",
      "\n",
      "Iterating through pc_alpha = [0.01]:\n",
      "\n",
      "# pc_alpha = 0.01 (1/1):\n",
      "\n",
      "Algorithm converged for variable s-dummy\n",
      "\n",
      "## Resulting lagged parent (super)sets:\n",
      "\n",
      "    Variable $X_0$ has 2 link(s):\n",
      "        (t-$C_4$ -1): max_pval = 0.00000, |min_val| =  2286.931\n",
      "        ($X_0$ -1): max_pval = 0.00000, |min_val| =  447.834\n",
      "\n",
      "    Variable $X_1$ has 1 link(s):\n",
      "        ($X_1$ -1): max_pval = 0.00000, |min_val| =  755.629\n",
      "\n",
      "    Variable $X_2$ has 5 link(s):\n",
      "        ($X_2$ -1): max_pval = 0.00000, |min_val| =  1023.086\n",
      "        ($X_1$ -2): max_pval = 0.00000, |min_val| =  800.010\n",
      "        (t-$C_4$ -1): max_pval = 0.00000, |min_val| =  633.770\n",
      "        ($X_2$ -2): max_pval = 0.00000, |min_val| =  105.613\n",
      "        (t-$C_4$ -2): max_pval = 0.00297, |min_val| =  8.823\n",
      "\n",
      "    Variable t-$C_4$ has 2 link(s):\n",
      "        (t-$C_4$ -2): max_pval = 0.00000, |min_val| =  33.834\n",
      "        (t-$C_4$ -1): max_pval = 0.00000, |min_val| =  28.687\n",
      "\n",
      "    Variable s-$C_5$ has 0 link(s):\n",
      "\n",
      "    Variable t-dummy has 0 link(s):\n",
      "\n",
      "    Variable s-dummy has 0 link(s):\n",
      "\n",
      "##\n",
      "## J-PCMCI+ Step 2: Discovering context-system links\n",
      "##\n",
      "\n",
      "With link_assumptions = {0: {(0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>'}, 1: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>'}, 2: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>'}, 3: {(3, -1): '-?>', (3, -2): '-?>', (0, 0): '<?-', (1, 0): '<?-', (2, 0): '<?-'}, 4: {(0, 0): '<?-', (1, 0): '<?-', (2, 0): '<?-'}, 5: {}, 6: {}}\n",
      "\n",
      "--------------------------\n",
      "Skeleton discovery phase\n",
      "--------------------------\n",
      "\n",
      "Testing contemporaneous condition sets of dimension 0: \n",
      "\n",
      "    Link (t-$C_4$  0) o?o $X_0$ (1/11):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ (t-$C_4$ -1) ($X_0$ -1) ]\n",
      "    with conds_x = [ (t-$C_4$ -2) (t-$C_4$ -1) ]\n",
      "    Subset 0: () gives pval = 0.31034 / val =  1.029\n",
      "    Non-significance detected.\n",
      "\n",
      "    Link (t-$C_4$ -1) -?> $X_0$ (2/11):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_0$ -1) ]\n",
      "    with conds_x = [ (t-$C_4$ -3) (t-$C_4$ -2) ]\n",
      "    Subset 0: () gives pval = 0.00000 / val =  2394.736\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link (t-$C_4$  0) o?o $X_1$ (3/11):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_1$ -1) ]\n",
      "    with conds_x = [ (t-$C_4$ -2) (t-$C_4$ -1) ]\n",
      "    Subset 0: () gives pval = 0.30614 / val =  1.047\n",
      "    Non-significance detected.\n",
      "\n",
      "    Link (t-$C_4$  0) o?o $X_2$ (4/11):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_2$ -1) ($X_1$ -2) (t-$C_4$ -1) ($X_2$ -2) (t-$C_4$ -2) ]\n",
      "    with conds_x = [ (t-$C_4$ -2) (t-$C_4$ -1) ]\n",
      "    Subset 0: () gives pval = 0.78048 / val =  0.078\n",
      "    Non-significance detected.\n",
      "\n",
      "    Link (t-$C_4$ -1) -?> $X_2$ (5/11):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_2$ -1) ($X_1$ -2) ($X_2$ -2) (t-$C_4$ -2) ]\n",
      "    with conds_x = [ (t-$C_4$ -3) (t-$C_4$ -2) ]\n",
      "    Subset 0: () gives pval = 0.00000 / val =  1029.781\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link (t-$C_4$ -2) -?> $X_2$ (6/11):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_2$ -1) ($X_1$ -2) (t-$C_4$ -1) ($X_2$ -2) ]\n",
      "    with conds_x = [ (t-$C_4$ -4) (t-$C_4$ -3) ]\n",
      "    Subset 0: () gives pval = 0.00000 / val =  21.292\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link (t-$C_4$ -1) -?> t-$C_4$ (7/11):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ (t-$C_4$ -2) ]\n",
      "    with conds_x = [ (t-$C_4$ -3) (t-$C_4$ -2) ]\n",
      "    Subset 0: () gives pval = 0.00000 / val =  23.622\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link (t-$C_4$ -2) -?> t-$C_4$ (8/11):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ (t-$C_4$ -1) ]\n",
      "    with conds_x = [ (t-$C_4$ -4) (t-$C_4$ -3) ]\n",
      "    Subset 0: () gives pval = 0.00000 / val =  31.039\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link (s-$C_5$  0) o?o $X_0$ (9/11):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ (t-$C_4$ -1) ($X_0$ -1) ]\n",
      "    with conds_x = [ ]\n",
      "    Subset 0: () gives pval = 0.50517 / val =  0.444\n",
      "    Non-significance detected.\n",
      "\n",
      "    Link (s-$C_5$  0) o?o $X_1$ (10/11):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_1$ -1) ]\n",
      "    with conds_x = [ ]\n",
      "    Subset 0: () gives pval = 0.22734 / val =  1.457\n",
      "    Non-significance detected.\n",
      "\n",
      "    Link (s-$C_5$  0) o?o $X_2$ (11/11):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_2$ -1) ($X_1$ -2) (t-$C_4$ -1) ($X_2$ -2) (t-$C_4$ -2) ]\n",
      "    with conds_x = [ ]\n",
      "    Subset 0: () gives pval = 0.00000 / val =  846.414\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "Updated contemp. adjacencies:\n",
      "\n",
      "    Variable $X_0$ has 2 link(s):\n",
      "        ($X_1$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        ($X_2$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "\n",
      "    Variable $X_1$ has 2 link(s):\n",
      "        ($X_0$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        ($X_2$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "\n",
      "    Variable $X_2$ has 3 link(s):\n",
      "        ($X_0$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        ($X_1$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        (s-$C_5$  0): max_pval = 0.00000, |min_val| =  846.414\n",
      "\n",
      "    Variable t-$C_4$ has 0 link(s):\n",
      "\n",
      "    Variable s-$C_5$ has 1 link(s):\n",
      "        ($X_2$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "\n",
      "    Variable t-dummy has 0 link(s):\n",
      "\n",
      "    Variable s-dummy has 0 link(s):\n",
      "\n",
      "Testing contemporaneous condition sets of dimension 1: \n",
      "\n",
      "    Link (t-$C_4$ -1) -?> $X_0$ (1/4):\n",
      "    Iterate through 2 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_0$ -1) ]\n",
      "    with conds_x = [ (t-$C_4$ -3) (t-$C_4$ -2) ]\n",
      "    Subset 0: ($X_1$ 0)  gives pval = 0.00000 / val =  2441.065\n",
      "    Subset 1: ($X_2$ 0)  gives pval = 0.00000 / val =  2178.587\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "    Link (t-$C_4$ -1) -?> $X_2$ (2/4):\n",
      "    Iterate through 3 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_2$ -1) ($X_1$ -2) ($X_2$ -2) (t-$C_4$ -2) ]\n",
      "    with conds_x = [ (t-$C_4$ -3) (t-$C_4$ -2) ]\n",
      "    Subset 0: ($X_0$ 0)  gives pval = 0.00000 / val =  642.041\n",
      "    Subset 1: ($X_1$ 0)  gives pval = 0.00000 / val =  1028.935\n",
      "    Subset 2: (s-$C_5$ 0)  gives pval = 0.00000 / val =  1219.155\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "    Link (t-$C_4$ -2) -?> $X_2$ (3/4):\n",
      "    Iterate through 3 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_2$ -1) ($X_1$ -2) (t-$C_4$ -1) ($X_2$ -2) ]\n",
      "    with conds_x = [ (t-$C_4$ -4) (t-$C_4$ -3) ]\n",
      "    Subset 0: ($X_0$ 0)  gives pval = 0.00000 / val =  22.028\n",
      "    Subset 1: ($X_1$ 0)  gives pval = 0.00000 / val =  21.199\n",
      "    Subset 2: (s-$C_5$ 0)  gives pval = 0.87332 / val =  0.025\n",
      "    Non-significance detected.\n",
      "\n",
      "    Link (s-$C_5$  0) o?o $X_2$ (4/4):\n",
      "    Iterate through 2 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_2$ -1) ($X_1$ -2) (t-$C_4$ -1) ($X_2$ -2) (t-$C_4$ -2) ]\n",
      "    with conds_x = [ ]\n",
      "    Subset 0: ($X_0$ 0)  gives pval = 0.00000 / val =  846.905\n",
      "    Subset 1: ($X_1$ 0)  gives pval = 0.00000 / val =  846.279\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "Updated contemp. adjacencies:\n",
      "\n",
      "    Variable $X_0$ has 2 link(s):\n",
      "        ($X_1$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        ($X_2$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "\n",
      "    Variable $X_1$ has 2 link(s):\n",
      "        ($X_0$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        ($X_2$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "\n",
      "    Variable $X_2$ has 3 link(s):\n",
      "        ($X_0$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        ($X_1$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        (s-$C_5$  0): max_pval = 0.00000, |min_val| =  846.279\n",
      "\n",
      "    Variable t-$C_4$ has 0 link(s):\n",
      "\n",
      "    Variable s-$C_5$ has 1 link(s):\n",
      "        ($X_2$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "\n",
      "    Variable t-dummy has 0 link(s):\n",
      "\n",
      "    Variable s-dummy has 0 link(s):\n",
      "\n",
      "Testing contemporaneous condition sets of dimension 2: \n",
      "\n",
      "    Link (t-$C_4$ -1) -?> $X_0$ (1/3):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_0$ -1) ]\n",
      "    with conds_x = [ (t-$C_4$ -3) (t-$C_4$ -2) ]\n",
      "    Subset 0: ($X_1$ 0) ($X_2$ 0)  gives pval = 0.00000 / val =  2201.104\n",
      "    No conditions of dimension 2 left.\n",
      "\n",
      "    Link (t-$C_4$ -1) -?> $X_2$ (2/3):\n",
      "    Iterate through 3 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_2$ -1) ($X_1$ -2) ($X_2$ -2) (t-$C_4$ -2) ]\n",
      "    with conds_x = [ (t-$C_4$ -3) (t-$C_4$ -2) ]\n",
      "    Subset 0: ($X_0$ 0) ($X_1$ 0)  gives pval = 0.00000 / val =  632.670\n",
      "    Subset 1: ($X_0$ 0) (s-$C_5$ 0)  gives pval = 0.00000 / val =  769.817\n",
      "    Subset 2: ($X_1$ 0) (s-$C_5$ 0)  gives pval = 0.00000 / val =  1218.181\n",
      "    No conditions of dimension 2 left.\n",
      "\n",
      "    Link (s-$C_5$  0) o?o $X_2$ (3/3):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_2$ -1) ($X_1$ -2) (t-$C_4$ -1) ($X_2$ -2) (t-$C_4$ -2) ]\n",
      "    with conds_x = [ ]\n",
      "    Subset 0: ($X_0$ 0) ($X_1$ 0)  gives pval = 0.00000 / val =  846.840\n",
      "    No conditions of dimension 2 left.\n",
      "\n",
      "Updated contemp. adjacencies:\n",
      "\n",
      "    Variable $X_0$ has 2 link(s):\n",
      "        ($X_1$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        ($X_2$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "\n",
      "    Variable $X_1$ has 2 link(s):\n",
      "        ($X_0$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        ($X_2$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "\n",
      "    Variable $X_2$ has 3 link(s):\n",
      "        ($X_0$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        ($X_1$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        (s-$C_5$  0): max_pval = 0.00000, |min_val| =  846.279\n",
      "\n",
      "    Variable t-$C_4$ has 0 link(s):\n",
      "\n",
      "    Variable s-$C_5$ has 1 link(s):\n",
      "        ($X_2$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "\n",
      "    Variable t-dummy has 0 link(s):\n",
      "\n",
      "    Variable s-dummy has 0 link(s):\n",
      "\n",
      "Testing contemporaneous condition sets of dimension 3: \n",
      "\n",
      "    Link (t-$C_4$ -1) -?> $X_2$ (1/1):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_2$ -1) ($X_1$ -2) ($X_2$ -2) (t-$C_4$ -2) ]\n",
      "    with conds_x = [ (t-$C_4$ -3) (t-$C_4$ -2) ]\n",
      "    Subset 0: ($X_0$ 0) ($X_1$ 0) (s-$C_5$ 0)  gives pval = 0.00000 / val =  758.806\n",
      "    No conditions of dimension 3 left.\n",
      "\n",
      "Updated contemp. adjacencies:\n",
      "\n",
      "    Variable $X_0$ has 2 link(s):\n",
      "        ($X_1$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        ($X_2$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "\n",
      "    Variable $X_1$ has 2 link(s):\n",
      "        ($X_0$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        ($X_2$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "\n",
      "    Variable $X_2$ has 3 link(s):\n",
      "        ($X_0$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        ($X_1$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        (s-$C_5$  0): max_pval = 0.00000, |min_val| =  846.279\n",
      "\n",
      "    Variable t-$C_4$ has 0 link(s):\n",
      "\n",
      "    Variable s-$C_5$ has 1 link(s):\n",
      "        ($X_2$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "\n",
      "    Variable t-dummy has 0 link(s):\n",
      "\n",
      "    Variable s-dummy has 0 link(s):\n",
      "\n",
      "Algorithm converged at p = 3.\n",
      "\n",
      "## Significant links at alpha = 0.01:\n",
      "\n",
      "    Variable $X_0$ has 4 link(s):\n",
      "        (t-$C_4$ -1): pval = 0.00000 | val =  2201.104\n",
      "        ($X_0$ -1): pval = 0.00000 | val =  0.000\n",
      "        ($X_1$  0): pval = 0.00000 | val =  0.000\n",
      "        ($X_2$  0): pval = 0.00000 | val =  0.000\n",
      "\n",
      "    Variable $X_1$ has 3 link(s):\n",
      "        ($X_0$  0): pval = 0.00000 | val =  0.000\n",
      "        ($X_1$ -1): pval = 0.00000 | val =  0.000\n",
      "        ($X_2$  0): pval = 0.00000 | val =  0.000\n",
      "\n",
      "    Variable $X_2$ has 7 link(s):\n",
      "        (s-$C_5$  0): pval = 0.00000 | val =  846.279\n",
      "        (t-$C_4$ -1): pval = 0.00000 | val =  632.670\n",
      "        ($X_0$  0): pval = 0.00000 | val =  0.000\n",
      "        ($X_1$  0): pval = 0.00000 | val =  0.000\n",
      "        ($X_1$ -2): pval = 0.00000 | val =  0.000\n",
      "        ($X_2$ -1): pval = 0.00000 | val =  0.000\n",
      "        ($X_2$ -2): pval = 0.00000 | val =  0.000\n",
      "\n",
      "    Variable t-$C_4$ has 2 link(s):\n",
      "        (t-$C_4$ -2): pval = 0.00000 | val =  31.039\n",
      "        (t-$C_4$ -1): pval = 0.00000 | val =  23.622\n",
      "\n",
      "    Variable s-$C_5$ has 1 link(s):\n",
      "        ($X_2$  0): pval = 0.00000 | val =  846.279\n",
      "\n",
      "    Variable t-dummy has 0 link(s):\n",
      "\n",
      "    Variable s-dummy has 0 link(s):\n",
      "\n",
      "##\n",
      "## J-PCMCI+ Step 3: Discovering dummy-system links\n",
      "##\n",
      "\n",
      "With link_assumptions = {0: {(0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (5, 0): '-?>', (6, 0): '-?>', (3, -1): '-->'}, 1: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (5, 0): '-?>', (6, 0): '-?>'}, 2: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, -1): '-?>', (2, -2): '-?>', (5, 0): '-?>', (6, 0): '-?>', (4, 0): '-->', (3, -1): '-->'}, 3: {(3, -1): '-->', (3, -2): '-->'}, 4: {(2, 0): '<--'}, 5: {(0, 0): '<?-', (1, 0): '<?-', (2, 0): '<?-'}, 6: {(0, 0): '<?-', (1, 0): '<?-', (2, 0): '<?-'}}\n",
      "\n",
      "--------------------------\n",
      "Skeleton discovery phase\n",
      "--------------------------\n",
      "\n",
      "Testing contemporaneous condition sets of dimension 0: \n",
      "\n",
      "    Link (t-dummy  0) o?o $X_0$ (1/6):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ (t-$C_4$ -1) ($X_0$ -1) ]\n",
      "    with conds_x = [ ]\n",
      "    Subset 0: () gives pval = 0.00000 / val =  1355.183\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link (t-dummy  0) o?o $X_1$ (2/6):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_1$ -1) ]\n",
      "    with conds_x = [ ]\n",
      "    Subset 0: () gives pval = 0.00000 / val =  664.241\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link (t-dummy  0) o?o $X_2$ (3/6):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ (s-$C_5$  0) (t-$C_4$ -1) ($X_2$ -1) ($X_1$ -2) ($X_2$ -2) ]\n",
      "    with conds_x = [ ]\n",
      "    Subset 0: () gives pval = 0.04208 / val =  121.196\n",
      "    Non-significance detected.\n",
      "\n",
      "    Link (s-dummy  0) o?o $X_0$ (4/6):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ (t-$C_4$ -1) ($X_0$ -1) ]\n",
      "    with conds_x = [ ]\n",
      "    Subset 0: () gives pval = 0.85041 / val =  39.739\n",
      "    Non-significance detected.\n",
      "\n",
      "    Link (s-dummy  0) o?o $X_1$ (5/6):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_1$ -1) ]\n",
      "    with conds_x = [ ]\n",
      "    Subset 0: () gives pval = 0.74694 / val =  43.029\n",
      "    Non-significance detected.\n",
      "\n",
      "    Link (s-dummy  0) o?o $X_2$ (6/6):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ (s-$C_5$  0) (t-$C_4$ -1) ($X_2$ -1) ($X_1$ -2) ($X_2$ -2) ]\n",
      "    with conds_x = [ ]\n",
      "    Subset 0: () gives pval = 0.23017 / val =  57.028\n",
      "    Non-significance detected.\n",
      "\n",
      "Updated contemp. adjacencies:\n",
      "\n",
      "    Variable $X_0$ has 3 link(s):\n",
      "        ($X_1$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        ($X_2$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        (t-dummy  0): max_pval = 0.00000, |min_val| =  1355.183\n",
      "\n",
      "    Variable $X_1$ has 3 link(s):\n",
      "        ($X_0$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        ($X_2$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        (t-dummy  0): max_pval = 0.00000, |min_val| =  664.241\n",
      "\n",
      "    Variable $X_2$ has 3 link(s):\n",
      "        ($X_0$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        ($X_1$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        (s-$C_5$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "\n",
      "    Variable t-$C_4$ has 0 link(s):\n",
      "\n",
      "    Variable s-$C_5$ has 1 link(s):\n",
      "        ($X_2$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "\n",
      "    Variable t-dummy has 2 link(s):\n",
      "        ($X_0$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        ($X_1$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "\n",
      "    Variable s-dummy has 0 link(s):\n",
      "\n",
      "Testing contemporaneous condition sets of dimension 1: \n",
      "\n",
      "    Link (t-dummy  0) o?o $X_0$ (1/2):\n",
      "    Iterate through 2 subset(s) of conditions: \n",
      "    with conds_y = [ (t-$C_4$ -1) ($X_0$ -1) ]\n",
      "    with conds_x = [ ]\n",
      "    Subset 0: ($X_1$ 0)  gives pval = 0.00000 / val =  1271.044\n",
      "    Subset 1: ($X_2$ 0)  gives pval = 0.00000 / val =  1355.270\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "    Link (t-dummy  0) o?o $X_1$ (2/2):\n",
      "    Iterate through 2 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_1$ -1) ]\n",
      "    with conds_x = [ ]\n",
      "    Subset 0: ($X_0$ 0)  gives pval = 0.00000 / val =  615.105\n",
      "    Subset 1: ($X_2$ 0)  gives pval = 0.00000 / val =  662.224\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "Updated contemp. adjacencies:\n",
      "\n",
      "    Variable $X_0$ has 3 link(s):\n",
      "        ($X_1$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        ($X_2$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        (t-dummy  0): max_pval = 0.00000, |min_val| =  1271.044\n",
      "\n",
      "    Variable $X_1$ has 3 link(s):\n",
      "        ($X_0$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        ($X_2$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        (t-dummy  0): max_pval = 0.00000, |min_val| =  615.105\n",
      "\n",
      "    Variable $X_2$ has 3 link(s):\n",
      "        ($X_0$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        ($X_1$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        (s-$C_5$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "\n",
      "    Variable t-$C_4$ has 0 link(s):\n",
      "\n",
      "    Variable s-$C_5$ has 1 link(s):\n",
      "        ($X_2$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "\n",
      "    Variable t-dummy has 2 link(s):\n",
      "        ($X_0$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        ($X_1$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "\n",
      "    Variable s-dummy has 0 link(s):\n",
      "\n",
      "Testing contemporaneous condition sets of dimension 2: \n",
      "\n",
      "    Link (t-dummy  0) o?o $X_0$ (1/2):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ (t-$C_4$ -1) ($X_0$ -1) ]\n",
      "    with conds_x = [ ]\n",
      "    Subset 0: ($X_1$ 0) ($X_2$ 0)  gives pval = 0.00000 / val =  1270.639\n",
      "    No conditions of dimension 2 left.\n",
      "\n",
      "    Link (t-dummy  0) o?o $X_1$ (2/2):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_1$ -1) ]\n",
      "    with conds_x = [ ]\n",
      "    Subset 0: ($X_0$ 0) ($X_2$ 0)  gives pval = 0.00000 / val =  602.844\n",
      "    No conditions of dimension 2 left.\n",
      "\n",
      "Updated contemp. adjacencies:\n",
      "\n",
      "    Variable $X_0$ has 3 link(s):\n",
      "        ($X_1$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        ($X_2$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        (t-dummy  0): max_pval = 0.00000, |min_val| =  1270.639\n",
      "\n",
      "    Variable $X_1$ has 3 link(s):\n",
      "        ($X_0$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        ($X_2$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        (t-dummy  0): max_pval = 0.00000, |min_val| =  602.844\n",
      "\n",
      "    Variable $X_2$ has 3 link(s):\n",
      "        ($X_0$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        ($X_1$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        (s-$C_5$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "\n",
      "    Variable t-$C_4$ has 0 link(s):\n",
      "\n",
      "    Variable s-$C_5$ has 1 link(s):\n",
      "        ($X_2$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "\n",
      "    Variable t-dummy has 2 link(s):\n",
      "        ($X_0$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "        ($X_1$  0): max_pval = 0.00000, |min_val| =  inf\n",
      "\n",
      "    Variable s-dummy has 0 link(s):\n",
      "\n",
      "Algorithm converged at p = 2.\n",
      "\n",
      "## Significant links at alpha = 0.01:\n",
      "\n",
      "    Variable $X_0$ has 5 link(s):\n",
      "        (t-$C_4$ -1): pval = 0.00000 | val =  2201.104\n",
      "        (t-dummy  0): pval = 0.00000 | val =  1270.639\n",
      "        ($X_0$ -1): pval = 0.00000 | val =  0.000\n",
      "        ($X_1$  0): pval = 0.00000 | val =  0.000\n",
      "        ($X_2$  0): pval = 0.00000 | val =  0.000\n",
      "\n",
      "    Variable $X_1$ has 4 link(s):\n",
      "        (t-dummy  0): pval = 0.00000 | val =  602.844\n",
      "        ($X_0$  0): pval = 0.00000 | val =  0.000\n",
      "        ($X_1$ -1): pval = 0.00000 | val =  0.000\n",
      "        ($X_2$  0): pval = 0.00000 | val =  0.000\n",
      "\n",
      "    Variable $X_2$ has 7 link(s):\n",
      "        (s-$C_5$  0): pval = 0.00000 | val =  846.279\n",
      "        (t-$C_4$ -1): pval = 0.00000 | val =  632.670\n",
      "        ($X_0$  0): pval = 0.00000 | val =  0.000\n",
      "        ($X_1$  0): pval = 0.00000 | val =  0.000\n",
      "        ($X_1$ -2): pval = 0.00000 | val =  0.000\n",
      "        ($X_2$ -1): pval = 0.00000 | val =  0.000\n",
      "        ($X_2$ -2): pval = 0.00000 | val =  0.000\n",
      "\n",
      "    Variable t-$C_4$ has 2 link(s):\n",
      "        (t-$C_4$ -1): pval = 0.00000 | val =  0.000\n",
      "        (t-$C_4$ -2): pval = 0.00000 | val =  0.000\n",
      "\n",
      "    Variable s-$C_5$ has 0 link(s):\n",
      "\n",
      "    Variable t-dummy has 2 link(s):\n",
      "        ($X_0$  0): pval = 0.00000 | val =  1270.639\n",
      "        ($X_1$  0): pval = 0.00000 | val =  602.844\n",
      "\n",
      "    Variable s-dummy has 0 link(s):\n",
      "\n",
      "##\n",
      "## J-PCMCI+ Step 4: Discovering system-system links \n",
      "##\n",
      "\n",
      "With link_assumptions = {0: {(0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (3, -1): '-->', (5, 0): '-->'}, 1: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (5, 0): '-->'}, 2: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, -1): '-?>', (2, -2): '-?>', (4, 0): '-->', (3, -1): '-->'}, 3: {}, 4: {(2, 0): '<--'}, 5: {(0, 0): '<--', (1, 0): '<--'}, 6: {}}\n",
      "\n",
      "--------------------------\n",
      "Skeleton discovery phase\n",
      "--------------------------\n",
      "\n",
      "Testing contemporaneous condition sets of dimension 0: \n",
      "\n",
      "    Link ($X_0$ -1) -?> $X_0$ (1/19):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ (t-$C_4$ -1) (t-dummy  0) ]\n",
      "    with conds_x = [ ($X_0$ -2) (t-$C_4$ -2) (t-dummy -1) ]\n",
      "    Subset 0: () gives pval = 0.00000 / val =  430.337\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link ($X_0$  0) o?o $X_1$ (2/19):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_1$ -1) (t-dummy  0) ]\n",
      "    with conds_x = [ ($X_0$ -1) (t-$C_4$ -1) (t-dummy  0) ]\n",
      "    Subset 0: () gives pval = 0.32655 / val =  0.963\n",
      "    Non-significance detected.\n",
      "\n",
      "    Link ($X_0$  0) o?o $X_2$ (3/19):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_2$ -1) ($X_1$ -2) ($X_2$ -2) (s-$C_5$  0) (t-$C_4$ -1) ]\n",
      "    with conds_x = [ ($X_0$ -1) (t-$C_4$ -1) (t-dummy  0) ]\n",
      "    Subset 0: () gives pval = 0.32165 / val =  0.982\n",
      "    Non-significance detected.\n",
      "\n",
      "    Link ($X_0$  0) o?o t-dummy (4/19):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ]\n",
      "    with conds_x = [ ($X_0$ -1) (t-$C_4$ -1) (t-dummy  0) ]\n",
      "    Subset 0: () gives pval = 0.00000 / val =  1.000\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link ($X_1$  0) o?o $X_0$ (5/19):\n",
      "    Already removed.\n",
      "\n",
      "    Link ($X_1$ -1) -?> $X_1$ (6/19):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ (t-dummy  0) ]\n",
      "    with conds_x = [ ($X_1$ -2) (t-dummy -1) ]\n",
      "    Subset 0: () gives pval = 0.00000 / val =  756.011\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link ($X_1$  0) o?o $X_2$ (7/19):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_2$ -1) ($X_1$ -2) ($X_2$ -2) (s-$C_5$  0) (t-$C_4$ -1) ]\n",
      "    with conds_x = [ ($X_1$ -1) (t-dummy  0) ]\n",
      "    Subset 0: () gives pval = 0.16792 / val =  1.901\n",
      "    Non-significance detected.\n",
      "\n",
      "    Link ($X_1$ -2) -?> $X_2$ (8/19):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_2$ -1) ($X_2$ -2) (s-$C_5$  0) (t-$C_4$ -1) ]\n",
      "    with conds_x = [ ($X_1$ -3) (t-dummy -2) ]\n",
      "    Subset 0: () gives pval = 0.00000 / val =  1050.392\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link ($X_1$  0) o?o t-dummy (9/19):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ]\n",
      "    with conds_x = [ ($X_1$ -1) (t-dummy  0) ]\n",
      "    Subset 0: () gives pval = 0.00000 / val =  1.000\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link ($X_2$  0) o?o $X_0$ (10/19):\n",
      "    Already removed.\n",
      "\n",
      "    Link ($X_2$  0) o?o $X_1$ (11/19):\n",
      "    Already removed.\n",
      "\n",
      "    Link ($X_2$ -1) -?> $X_2$ (12/19):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_1$ -2) ($X_2$ -2) (s-$C_5$  0) (t-$C_4$ -1) ]\n",
      "    with conds_x = [ ($X_2$ -2) ($X_1$ -3) ($X_2$ -3) (s-$C_5$ -1) (t-$C_4$ -2) ]\n",
      "    Subset 0: () gives pval = 0.00000 / val =  425.875\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link ($X_2$ -2) -?> $X_2$ (13/19):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_2$ -1) ($X_1$ -2) (s-$C_5$  0) (t-$C_4$ -1) ]\n",
      "    with conds_x = [ ($X_2$ -3) ($X_1$ -4) ($X_2$ -4) (s-$C_5$ -2) (t-$C_4$ -3) ]\n",
      "    Subset 0: () gives pval = 0.02313 / val =  5.159\n",
      "    Non-significance detected.\n",
      "\n",
      "    Link ($X_2$  0) o?o s-$C_5$ (14/19):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ]\n",
      "    with conds_x = [ ($X_2$ -1) ($X_1$ -2) ($X_2$ -2) (s-$C_5$  0) (t-$C_4$ -1) ]\n",
      "    Subset 0: () gives pval = 0.00000 / val =  1.000\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link (t-$C_4$ -1) -?> $X_0$ (15/19):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_0$ -1) (t-dummy  0) ]\n",
      "    with conds_x = [ ]\n",
      "    Subset 0: () gives pval = 0.00000 / val =  1.000\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link (t-$C_4$ -1) -?> $X_2$ (16/19):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_2$ -1) ($X_1$ -2) ($X_2$ -2) (s-$C_5$  0) ]\n",
      "    with conds_x = [ ]\n",
      "    Subset 0: () gives pval = 0.00000 / val =  1.000\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link (s-$C_5$  0) o?o $X_2$ (17/19):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_2$ -1) ($X_1$ -2) ($X_2$ -2) (t-$C_4$ -1) ]\n",
      "    with conds_x = [ ]\n",
      "    Subset 0: () gives pval = 0.00000 / val =  1.000\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link (t-dummy  0) o?o $X_0$ (18/19):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_0$ -1) (t-$C_4$ -1) ]\n",
      "    with conds_x = [ ]\n",
      "    Subset 0: () gives pval = 0.00000 / val =  1.000\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "    Link (t-dummy  0) o?o $X_1$ (19/19):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_1$ -1) ]\n",
      "    with conds_x = [ ]\n",
      "    Subset 0: () gives pval = 0.00000 / val =  1.000\n",
      "    No conditions of dimension 0 left.\n",
      "\n",
      "Updated contemp. adjacencies:\n",
      "\n",
      "    Variable $X_0$ has 1 link(s):\n",
      "        (t-dummy  0): max_pval = 0.00000, |min_val| =  1.000\n",
      "\n",
      "    Variable $X_1$ has 1 link(s):\n",
      "        (t-dummy  0): max_pval = 0.00000, |min_val| =  1.000\n",
      "\n",
      "    Variable $X_2$ has 1 link(s):\n",
      "        (s-$C_5$  0): max_pval = 0.00000, |min_val| =  1.000\n",
      "\n",
      "    Variable t-$C_4$ has 0 link(s):\n",
      "\n",
      "    Variable s-$C_5$ has 1 link(s):\n",
      "        ($X_2$  0): max_pval = 0.00000, |min_val| =  1.000\n",
      "\n",
      "    Variable t-dummy has 2 link(s):\n",
      "        ($X_0$  0): max_pval = 0.00000, |min_val| =  1.000\n",
      "        ($X_1$  0): max_pval = 0.00000, |min_val| =  1.000\n",
      "\n",
      "    Variable s-dummy has 0 link(s):\n",
      "\n",
      "Testing contemporaneous condition sets of dimension 1: \n",
      "\n",
      "    Link ($X_0$ -1) -?> $X_0$ (1/8):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ (t-$C_4$ -1) (t-dummy  0) ]\n",
      "    with conds_x = [ ($X_0$ -2) (t-$C_4$ -2) (t-dummy -1) ]\n",
      "    Subset 0: (t-dummy 0)  gives pval = 0.00000 / val =  430.337\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "    Link ($X_0$  0) o?o t-dummy (2/8):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ]\n",
      "    with conds_x = [ ($X_0$ -1) (t-$C_4$ -1) (t-dummy  0) ]\n",
      "    Subset 0: ($X_1$ 0)  gives pval = 0.00000 / val =  1.000\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "    Link ($X_1$ -1) -?> $X_1$ (3/8):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ (t-dummy  0) ]\n",
      "    with conds_x = [ ($X_1$ -2) (t-dummy -1) ]\n",
      "    Subset 0: (t-dummy 0)  gives pval = 0.00000 / val =  756.011\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "    Link ($X_1$ -2) -?> $X_2$ (4/8):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_2$ -1) ($X_2$ -2) (s-$C_5$  0) (t-$C_4$ -1) ]\n",
      "    with conds_x = [ ($X_1$ -3) (t-dummy -2) ]\n",
      "    Subset 0: (s-$C_5$ 0)  gives pval = 0.00000 / val =  1050.392\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "    Link ($X_1$  0) o?o t-dummy (5/8):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ]\n",
      "    with conds_x = [ ($X_1$ -1) (t-dummy  0) ]\n",
      "    Subset 0: ($X_0$ 0)  gives pval = 0.00000 / val =  1.000\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "    Link ($X_2$ -1) -?> $X_2$ (6/8):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_1$ -2) ($X_2$ -2) (s-$C_5$  0) (t-$C_4$ -1) ]\n",
      "    with conds_x = [ ($X_2$ -2) ($X_1$ -3) ($X_2$ -3) (s-$C_5$ -1) (t-$C_4$ -2) ]\n",
      "    Subset 0: (s-$C_5$ 0)  gives pval = 0.00000 / val =  425.875\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "    Link (t-$C_4$ -1) -?> $X_0$ (7/8):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_0$ -1) (t-dummy  0) ]\n",
      "    with conds_x = [ ]\n",
      "    Subset 0: (t-dummy 0)  gives pval = 0.00000 / val =  1.000\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "    Link (t-$C_4$ -1) -?> $X_2$ (8/8):\n",
      "    Iterate through 1 subset(s) of conditions: \n",
      "    with conds_y = [ ($X_2$ -1) ($X_1$ -2) ($X_2$ -2) (s-$C_5$  0) ]\n",
      "    with conds_x = [ ]\n",
      "    Subset 0: (s-$C_5$ 0)  gives pval = 0.00000 / val =  1.000\n",
      "    No conditions of dimension 1 left.\n",
      "\n",
      "Updated contemp. adjacencies:\n",
      "\n",
      "    Variable $X_0$ has 1 link(s):\n",
      "        (t-dummy  0): max_pval = 0.00000, |min_val| =  1.000\n",
      "\n",
      "    Variable $X_1$ has 1 link(s):\n",
      "        (t-dummy  0): max_pval = 0.00000, |min_val| =  1.000\n",
      "\n",
      "    Variable $X_2$ has 1 link(s):\n",
      "        (s-$C_5$  0): max_pval = 0.00000, |min_val| =  1.000\n",
      "\n",
      "    Variable t-$C_4$ has 0 link(s):\n",
      "\n",
      "    Variable s-$C_5$ has 1 link(s):\n",
      "        ($X_2$  0): max_pval = 0.00000, |min_val| =  1.000\n",
      "\n",
      "    Variable t-dummy has 2 link(s):\n",
      "        ($X_0$  0): max_pval = 0.00000, |min_val| =  1.000\n",
      "        ($X_1$  0): max_pval = 0.00000, |min_val| =  1.000\n",
      "\n",
      "    Variable s-dummy has 0 link(s):\n",
      "\n",
      "Algorithm converged at p = 1.\n",
      "\n",
      "----------------------------\n",
      "Collider orientation phase\n",
      "----------------------------\n",
      "\n",
      "contemp_collider_rule = majority\n",
      "conflict_resolution = True\n",
      "\n",
      "\n",
      "Updated adjacencies:\n",
      "\n",
      "    Variable $X_0$ has 3 link(s):\n",
      "        ($X_0$ -1)\n",
      "        (t-$C_4$ -1)\n",
      "        (t-dummy  0)\n",
      "\n",
      "    Variable $X_1$ has 2 link(s):\n",
      "        ($X_1$ -1)\n",
      "        (t-dummy  0)\n",
      "\n",
      "    Variable $X_2$ has 4 link(s):\n",
      "        ($X_1$ -2)\n",
      "        ($X_2$ -1)\n",
      "        (t-$C_4$ -1)\n",
      "        (s-$C_5$  0)\n",
      "\n",
      "    Variable t-$C_4$ has 0 link(s):\n",
      "\n",
      "    Variable s-$C_5$ has 1 link(s):\n",
      "        ($X_2$  0)\n",
      "\n",
      "    Variable t-dummy has 2 link(s):\n",
      "        ($X_0$  0)\n",
      "        ($X_1$  0)\n",
      "\n",
      "    Variable s-dummy has 0 link(s):\n",
      "\n",
      "\n",
      "----------------------------\n",
      "Rule orientation phase\n",
      "----------------------------\n",
      "\n",
      "Try rule(s) [1 2 3]\n",
      "\n",
      "Updated adjacencies:\n",
      "\n",
      "    Variable $X_0$ has 3 link(s):\n",
      "        ($X_0$ -1)\n",
      "        (t-$C_4$ -1)\n",
      "        (t-dummy  0)\n",
      "\n",
      "    Variable $X_1$ has 2 link(s):\n",
      "        ($X_1$ -1)\n",
      "        (t-dummy  0)\n",
      "\n",
      "    Variable $X_2$ has 4 link(s):\n",
      "        ($X_1$ -2)\n",
      "        ($X_2$ -1)\n",
      "        (t-$C_4$ -1)\n",
      "        (s-$C_5$  0)\n",
      "\n",
      "    Variable t-$C_4$ has 0 link(s):\n",
      "\n",
      "    Variable s-$C_5$ has 1 link(s):\n",
      "        ($X_2$  0)\n",
      "\n",
      "    Variable t-dummy has 2 link(s):\n",
      "        ($X_0$  0)\n",
      "        ($X_1$  0)\n",
      "\n",
      "    Variable s-dummy has 0 link(s):\n",
      "\n",
      "## Significant links at alpha = 0.01:\n",
      "\n",
      "    Variable $X_0$ has 3 link(s):\n",
      "        (t-$C_4$ -1): pval = 0.00000 | val =  2201.104\n",
      "        (t-dummy  0): pval = 0.00000 | val =  1270.639\n",
      "        ($X_0$ -1): pval = 0.00000 | val =  430.337\n",
      "\n",
      "    Variable $X_1$ has 2 link(s):\n",
      "        ($X_1$ -1): pval = 0.00000 | val =  756.011\n",
      "        (t-dummy  0): pval = 0.00000 | val =  602.844\n",
      "\n",
      "    Variable $X_2$ has 4 link(s):\n",
      "        ($X_1$ -2): pval = 0.00000 | val =  1050.392\n",
      "        (s-$C_5$  0): pval = 0.00000 | val =  846.279\n",
      "        (t-$C_4$ -1): pval = 0.00000 | val =  632.670\n",
      "        ($X_2$ -1): pval = 0.00000 | val =  425.875\n",
      "\n",
      "    Variable t-$C_4$ has 0 link(s):\n",
      "\n",
      "    Variable s-$C_5$ has 0 link(s):\n",
      "\n",
      "    Variable t-dummy has 0 link(s):\n",
      "\n",
      "    Variable s-dummy has 0 link(s):\n"
     ]
    }
   ],
   "source": [
    "# Create a J-PCMCI+ object, passing the dataframe and (conditional)\n",
    "# independence test objects, as well as the observed temporal and spatial context nodes \n",
    "# and the indices of the dummies.\n",
    "\n",
    "jpcmciplus_regr = JPCMCIplus(dataframe=dataframe_regr,\n",
    "                          cond_ind_test=RegressionCI(significance='analytic'),\n",
    "                          node_classification=node_classification_jpcmci,\n",
    "                          verbosity=2,)\n",
    "\n",
    "# Define the analysis parameters.\n",
    "tau_max = 2\n",
    "pc_alpha = 0.01\n",
    "\n",
    "# Run J-PCMCI+\n",
    "results_regr = jpcmciplus_regr.run_jpcmciplus(tau_min=0, \n",
    "                              tau_max=tau_max, \n",
    "                              pc_alpha=pc_alpha)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7ef2707",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "#### Plotting the results\n",
    "Next, we use the function ```tp.plot_graph``` to plot the learned summary DAG. In our example the learned graph exactly agrees with the true DAG. \n",
    "\n",
    "Note, in particular, that J-PCMCI+ has correctly identified the links between the observed context variables and the system variables. It also managed to deconfound $X^0$ and $X^2$ which are confounded by an unobserved temporal context variable ($L^4_t$) using the time dummy.\n",
    "\n",
    "The edges are colored according to the test statistic values returned by ```JPCMCIplus.run_jpcmciplus```, i.e., the maximum absolute value of ```val_matrix[i, j, tau]``` determines the color of the edge between $X^i_{t-\\tau}$ and $X^j_t$ (provided this edge exists) according to the color scale at the bottom. The color of node denotes the maximum of the absolute value of ```val_matrix[i, i, tau]``` across all $1 \\leq \\tau \\leq \\tau_\\text{max}$.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5751be80a0dd7df",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "###### 1. With a variant of ParCorrMult"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9f69d0bd89f67b7e",
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-12-04T07:29:31.979432704Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tp.plot_graph(results['graph'], val_matrix=results['val_matrix'], var_names=var_names)\n",
    "tp.plot_time_series_graph(results['graph'], val_matrix = results['val_matrix'], \n",
    "              node_classification = node_classification_jpcmci, var_names=var_names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac0aca698ff400a0",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "###### 2. With RegressionCI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "95daef9f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-04T07:29:43.661935530Z",
     "start_time": "2023-12-04T07:29:40.871534689Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tp.plot_graph(results_regr['graph'], val_matrix=results_regr['val_matrix'], var_names=var_names)\n",
    "tp.plot_time_series_graph(results_regr['graph'], val_matrix = results_regr['val_matrix'], \n",
    "              node_classification = node_classification_jpcmci, var_names=var_names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14f1b107",
   "metadata": {},
   "source": [
    "If we do not include the dummy variables, we see that $X^1$ and $X^0$ are confounded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "92babc1a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-04T07:29:45.160111871Z",
     "start_time": "2023-12-04T07:29:43.672624361Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##\n",
      "## J-PCMCI+ Step 1: Selecting lagged conditioning sets\n",
      "##\n",
      "\n",
      "##\n",
      "## Step 1: PC1 algorithm for selecting lagged conditions\n",
      "##\n",
      "\n",
      "Parameters:\n",
      "link_assumptions = {0: {(0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>'}, 1: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>'}, 2: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>'}, 3: {(3, -1): '-?>', (3, -2): '-?>'}, 4: {}}\n",
      "independence test = par_corr_mult\n",
      "tau_min = 1\n",
      "tau_max = 2\n",
      "pc_alpha = [0.01]\n",
      "max_conds_dim = None\n",
      "max_combinations = 1\n",
      "\n",
      "\n",
      "## Resulting lagged parent (super)sets:\n",
      "\n",
      "    Variable $X_0$ has 2 link(s):\n",
      "        (t-$C_4$ -1): max_pval = 0.00000, |min_val| =  0.616\n",
      "        ($X_0$ -1): max_pval = 0.00000, |min_val| =  0.298\n",
      "\n",
      "    Variable $X_1$ has 1 link(s):\n",
      "        ($X_1$ -1): max_pval = 0.00000, |min_val| =  0.382\n",
      "\n",
      "    Variable $X_2$ has 5 link(s):\n",
      "        ($X_2$ -1): max_pval = 0.00000, |min_val| =  0.438\n",
      "        ($X_1$ -2): max_pval = 0.00000, |min_val| =  0.392\n",
      "        (t-$C_4$ -1): max_pval = 0.00000, |min_val| =  0.352\n",
      "        ($X_2$ -2): max_pval = 0.00000, |min_val| =  0.148\n",
      "        (t-$C_4$ -2): max_pval = 0.00300, |min_val| =  0.043\n",
      "\n",
      "    Variable t-$C_4$ has 2 link(s):\n",
      "        (t-$C_4$ -2): max_pval = 0.00000, |min_val| =  0.084\n",
      "        (t-$C_4$ -1): max_pval = 0.00000, |min_val| =  0.077\n",
      "\n",
      "    Variable s-$C_5$ has 0 link(s):\n",
      "\n",
      "##\n",
      "## J-PCMCI+ Step 2: Discovering context-system links\n",
      "##\n",
      "\n",
      "With link_assumptions = {0: {(0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>'}, 1: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>'}, 2: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>'}, 3: {(3, -1): '-?>', (3, -2): '-?>', (0, 0): '<?-', (1, 0): '<?-', (2, 0): '<?-'}, 4: {(0, 0): '<?-', (1, 0): '<?-', (2, 0): '<?-'}}\n",
      "\n",
      "## Significant links at alpha = 0.01:\n",
      "\n",
      "    Variable $X_0$ has 4 link(s):\n",
      "        (t-$C_4$ -1): pval = 0.00000 | val =  0.606\n",
      "        ($X_0$ -1): pval = 0.00000 | val =  0.000\n",
      "        ($X_1$  0): pval = 0.00000 | val =  0.000\n",
      "        ($X_2$  0): pval = 0.00000 | val =  0.000\n",
      "\n",
      "    Variable $X_1$ has 3 link(s):\n",
      "        ($X_0$  0): pval = 0.00000 | val =  0.000\n",
      "        ($X_1$ -1): pval = 0.00000 | val =  0.000\n",
      "        ($X_2$  0): pval = 0.00000 | val =  0.000\n",
      "\n",
      "    Variable $X_2$ has 7 link(s):\n",
      "        (s-$C_5$  0): pval = 0.00000 | val =  0.402\n",
      "        (t-$C_4$ -1): pval = 0.00000 | val =  0.351\n",
      "        ($X_0$  0): pval = 0.00000 | val =  0.000\n",
      "        ($X_1$  0): pval = 0.00000 | val =  0.000\n",
      "        ($X_1$ -2): pval = 0.00000 | val =  0.000\n",
      "        ($X_2$ -1): pval = 0.00000 | val =  0.000\n",
      "        ($X_2$ -2): pval = 0.00000 | val =  0.000\n",
      "\n",
      "    Variable t-$C_4$ has 2 link(s):\n",
      "        (t-$C_4$ -2): pval = 0.00000 | val =  0.080\n",
      "        (t-$C_4$ -1): pval = 0.00000 | val =  0.070\n",
      "\n",
      "    Variable s-$C_5$ has 1 link(s):\n",
      "        ($X_2$  0): pval = 0.00000 | val =  0.402\n",
      "\n",
      "##\n",
      "## J-PCMCI+ Step 4: Discovering system-system links \n",
      "##\n",
      "\n",
      "With link_assumptions = {0: {(0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (3, -1): '-->'}, 1: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>'}, 2: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, -1): '-?>', (2, -2): '-?>', (4, 0): '-->', (3, -1): '-->'}, 3: {}, 4: {(2, 0): '<--'}}\n",
      "\n",
      "## Significant links at alpha = 0.01:\n",
      "\n",
      "    Variable $X_0$ has 3 link(s):\n",
      "        (t-$C_4$ -1): pval = 0.00000 | val =  0.606\n",
      "        ($X_0$ -1): pval = 0.00000 | val =  0.262\n",
      "        ($X_1$  0): pval = 0.00000 | val =  0.156 | unclear orientation due to conflict\n",
      "\n",
      "    Variable $X_1$ has 2 link(s):\n",
      "        ($X_1$ -1): pval = 0.00000 | val =  0.382\n",
      "        ($X_0$  0): pval = 0.00000 | val =  0.156 | unclear orientation due to conflict\n",
      "\n",
      "    Variable $X_2$ has 4 link(s):\n",
      "        ($X_1$ -2): pval = 0.00000 | val = -0.461\n",
      "        (s-$C_5$  0): pval = 0.00000 | val =  0.402\n",
      "        (t-$C_4$ -1): pval = 0.00000 | val =  0.351\n",
      "        ($X_2$ -1): pval = 0.00000 | val =  0.291\n",
      "\n",
      "    Variable t-$C_4$ has 0 link(s):\n",
      "\n",
      "    Variable s-$C_5$ has 0 link(s):\n",
      "\n",
      "## Ambiguous triples (not used for orientation):\n",
      "\n",
      "    [($X_0$ -1), $X_0$, $X_1$]\n"
     ]
    }
   ],
   "source": [
    "# without dummy\n",
    "# Classify all the nodes into system, context, or dummy\n",
    "node_classification_jpcmci_wo_dummy = {i: node_classification[var] for i, var in enumerate(observed_indices)}\n",
    "\n",
    "# Name all the variables and initialize the dataframe object\n",
    "# Be careful to use analysis_mode = 'multiple'\n",
    "var_names_wo_dummy = sys_var_names + context_var_names \n",
    "\n",
    "\n",
    "dataframe = pp.DataFrame(\n",
    "    data=data_observed,\n",
    "    analysis_mode = 'multiple',\n",
    "    var_names = var_names_wo_dummy\n",
    "    )\n",
    "\n",
    "# Create a J-PCMCI+ object, passing the dataframe and (conditional)\n",
    "# independence test objects, as well as the observed temporal and spatial context nodes \n",
    "# and the indices of the dummies.\n",
    "jpcmciplus = JPCMCIplus(dataframe=dataframe,\n",
    "                          cond_ind_test=ParCorrMult(significance='analytic'), \n",
    "                          verbosity=1,\n",
    "                          node_classification=node_classification_jpcmci_wo_dummy)\n",
    "\n",
    "\n",
    "# Define the analysis parameters.\n",
    "tau_max = 2\n",
    "pc_alpha = 0.01\n",
    "\n",
    "\n",
    "# Run J-PCMCI+\n",
    "results = jpcmciplus.run_jpcmciplus(tau_min=0, \n",
    "                              tau_max=tau_max, \n",
    "                              pc_alpha=pc_alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "70e4ef8b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-04T07:29:46.013134149Z",
     "start_time": "2023-12-04T07:29:45.166324724Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tp.plot_graph(results['graph'], val_matrix=results['val_matrix'], var_names=var_names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fc60582",
   "metadata": {},
   "source": [
    "The confounded link ``x-x`` with the `x`-edgemarks indicates that the orientation phase resulted in conflicting orientations when different rules where applied, see the PCMCIplus tutorial for details.\n",
    "\n",
    "We suggest to first study toymodels that mimic the datasets of interest, but where causal ground truth is known. This helps to assess whether J-PCMCI$^+$ is applicable given, eg, too small sample sizes, and how to choose hyperparameters."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65be5017",
   "metadata": {},
   "source": [
    "#### References\n",
    "\n",
    "- [1] Joris M. Mooij, Sara Magliacane, and Tom Claassen. \"Joint causal inference from multiple contexts.\" The Journal of Machine Learning Research 21.1 (2020): 3919-4026.\n",
    "- [2] Biwei Huang, et al. \"Causal discovery from heterogeneous/nonstationary data.\" The Journal of Machine Learning Research 21.1 (2020): 3482-3534."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f59150ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-04T07:29:46.015842687Z",
     "start_time": "2023-12-04T07:29:46.013399980Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "278d3762",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-04T07:29:46.063092275Z",
     "start_time": "2023-12-04T07:29:46.017749720Z"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tigenv",
   "language": "python",
   "name": "tigenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
